In this script we conduct the estimation for the measure_arguments approach.

PROGRAMS=pg_arguments_full5_c200_opc15x2 SAMPLESIZE=50 NSAMPLES=1`.

Expected a result file nethermind_pg_arguments_full5_c200_opc15x2_a.csv.

# the programs file is too large to be placed in github
programs = read.csv(paste("../../local/", program_set_codename, ".csv", sep=""))

results = load_data_set(env, program_set_codename, measurement_codename)
# besu may have additional columns with gc stats
results = results[, c("program_id", "sample_id", "run_id", "measure_total_time_ns", "measure_total_timer_time_ns", "env")]
# TODO geth short-circuits zero length programs, resulting in zero timing somehow. Drop these more elegantly, not based on measure_total_time_ns
results = results[which(results$measure_total_time_ns != 0), ]

all_envs = c(env)
measurements = sqldf("SELECT opcode, op_count, arg0, arg1, arg2, sample_id, run_id, measure_total_time_ns, env, results.program_id
                     FROM results
                     INNER JOIN
                       programs ON(results.program_id = programs.program_id)
                     ")
measurements$opcode = factor(measurements$opcode, levels=unique(programs$opcode))
head(measurements)
##   opcode op_count arg0 arg1 arg2 sample_id run_id measure_total_time_ns
## 1    ADD        0   25   27   NA         0      1              11809.71
## 2    ADD       15   25   27   NA         0      1              11341.53
## 3    ADD       30   25   27   NA         0      1              12562.83
## 4    ADD        0   14    9   NA         0      1              11796.06
## 5    ADD       15   14    9   NA         0      1              12456.87
## 6    ADD       30   14    9   NA         0      1              12173.68
##          env program_id
## 1 nethermind      ADD_0
## 2 nethermind      ADD_1
## 3 nethermind      ADD_2
## 4 nethermind      ADD_3
## 5 nethermind      ADD_4
## 6 nethermind      ADD_5

Remove outliers if needed.

# Extracts all OPCODEs from the `programs` data frame of the given arity (args taken off the stack).
extract_opcodes <- function(arity) {
  if (!missing(arity)) {
    if (arity == 0) {
      programs = programs[which(is.na(programs$arg0) & is.na(programs$arg1) & is.na(programs$arg2)), ]
    }
    if (arity == 1) {
      programs = programs[which(!is.na(programs$arg0) & is.na(programs$arg1) & is.na(programs$arg2)), ]
    }
    if (arity == 2) {
      programs = programs[which(!is.na(programs$arg1) & is.na(programs$arg2)), ]
    }
    if (arity == 3) {
      programs = programs[which(!is.na(programs$arg2)), ]
    }
  }
  unique(programs$opcode)
}
if ( (!removed_outliers) && (!removed_outliers_2)) {
  boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'all'))
}
if (removed_outliers) {
  par(mfrow=c(length(all_envs)*2, 1))
  
  # before
  boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'all'))

  measurements = remove_outliers(measurements, 'measure_total_time_ns', FALSE)
  
  # after
  boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'no_outliers'))
}
all_opcodes = extract_opcodes()
nullary_opcodes = extract_opcodes(0)
unary_opcodes = extract_opcodes(1)
binary_opcodes = extract_opcodes(2)
ternary_opcodes = extract_opcodes(3)

div_opcodes = c('DIV', 'MOD', 'SDIV', 'SMOD')
measurements$expensive = NA
measurements[which(measurements$opcode %in% div_opcodes), ]$expensive =
  measurements[which(measurements$opcode %in% div_opcodes), ]$arg0 >
  measurements[which(measurements$opcode %in% div_opcodes), ]$arg1
# remember that argX is the byte-size of the argument in these measurements
measurements[which(measurements$opcode == 'ADDMOD'), ]$expensive =
  8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg0 +
  8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg1 > 
  8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg2
measurements[which(measurements$opcode == 'MULMOD'), ]$expensive =
  measurements[which(measurements$opcode == 'MULMOD'), ]$arg0 +
  measurements[which(measurements$opcode == 'MULMOD'), ]$arg1 >
  measurements[which(measurements$opcode == 'MULMOD'), ]$arg2
if (removed_outliers_2) {
  measurements = remove_compare_outliers(measurements, 'measure_total_time_ns', all_envs)
}

Detailed view

This is massive and detailed overview on the impact of arguments. Because of the number of charts, only op count = 30 is eligible. Feel free to change it, but that should not be anyhow more informative. The visualizations do not guarantee that all dependencies are clearly seen. Especially for binary and ternary opcodes where impacts of arg0, arg1 and arg2 are mixed. But if a dependency is graphically noticeable that you should expect also statistical dependency.

for (env in all_envs) {
  for (opcode in unary_opcodes) {
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg0', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg0', 'opcount 15'))
    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg0', 'opcount 30'))
  } 
  for (opcode in binary_opcodes) {
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg0', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg0', 'opcount 15'))
    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg0', 'opcount 30'))
#    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg1', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg1', 'opcount 15'))
    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg1', 'opcount 30'))
  } 
  for (opcode in ternary_opcodes) {
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg0', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg0', 'opcount 15'))
    plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg0', 'opcount 30'))
#    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg1', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg1', 'opcount 15'))
    plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg1', 'opcount 30'))
#    plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
#    title(main = paste(env, opcode, 'arg2', 'opcount 0'))
#    plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
#    title(main = paste(env, opcode, 'arg2', 'opcount 15'))
    plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
    title(main = paste(env, opcode, 'arg2', 'opcount 30'))
  } 
}

Models

Notes: 1. Outliers need to be removed if detected 2. The argX:op_count interactions measure the impact on the OPCODE 3. The argX are just auxiliary variables added to exclude the effect of cheaper/more expensive PUSHes. We only want to extract the effect of the argument on the measured OPCODE repeated op_count times.

# Every `arg` coefficient represents the impact of the argument's byte size growing by 1.
# We treat as impactful the arguments where p-value is effectively zero. The previous approach was:
# Treat as impactful the arguments, where:
# 1. The estimate is significant with confidence 0.001
# 2. The increase of arg's byte size by 1 will increase the cost by more than 1%
# but it turned out to be much less stable in practice.
p_value_thresh = 1e-30
# p_value_thresh = 0.001
impact_ratio = 0.00
# impact_ratio = 0.01

arg_lm <- function(df, opcode, env, formula) {
  data = df[which(df$opcode==opcode & df$env==env), ]
  lm(formula, data=data)
}

# Adds the results from the estimated `model` to the `results_df` data frame.
# You need to provide the corresponding `opcode`, `env` and `arity`.
# `results_df` is assumed to have the columns as the `first_pass` data frame has (see below)
add_arg_results <- function(model, opcode, env, results_df, arity) {
  stopifnot(arity > 0)

  all_coefficients = summary(model)$coefficients
  arg_coefficients = all_coefficients[!(row.names(all_coefficients) %in% c("op_count", "(Intercept)", "arg0", "arg1", "arg2")),]
  pure_op_count_coeff = all_coefficients["op_count", 1]
  # will be filled if any is impacting
  args_ns = c(NA, NA, NA)
  # will be always if arg present
  args_ns_raw = c(NA, NA, NA)
  args_ns_p = c(NA, NA, NA)

  if (arity == 1) {
    # there's only one arg coefficient here, silly R forces us to take a special case path...
    has_significant = arg_coefficients[4] < p_value_thresh
  
    if (has_significant) {
      coefficient_impact = abs(arg_coefficients[1])
      has_impacting = has_significant & coefficient_impact > pure_op_count_coeff * impact_ratio
    } else {
      has_impacting = FALSE
    }
    if (has_impacting) {
      args_ns[1] = arg_coefficients[1]
    }
    args_ns_raw[1] = arg_coefficients[1]
    args_ns_p[1] = arg_coefficients[4]
  } else {
    significant = arg_coefficients[, 4] < p_value_thresh
    has_significant = length(which(significant)) > 0
  
    coefficient_impact = abs(arg_coefficients[, 1])
    can_impact = significant & coefficient_impact > pure_op_count_coeff * impact_ratio
    has_impacting = length(which(can_impact)) > 0
    args_ns[which(can_impact)] = arg_coefficients[which(can_impact), 1]
    args_ns_raw[1:arity] = arg_coefficients[1:arity, 1]
    args_ns_p[1:arity] = arg_coefficients[1:arity, 4]
  }
  
  # NAs for the "expensive" arg columns. See above for the columns layout
  results_df[nrow(results_df) + 1, ] = c(opcode, env, has_significant, has_impacting, pure_op_count_coeff, args_ns, NA, args_ns_raw, NA, args_ns_p, NA)
  return(results_df)
}

# Adds the results from the estimated `model` to the `results_df` data frame, where the model is
# specifically the one gauged towards the "division" OPCODEs like `DIV`.
# See also `add_arg_results`
add_arg_expensive_results <- function(model, opcode, env, results_df, arity) {
  stopifnot(arity > 0)

  all_coefficients = summary(model)$coefficients
  pure_op_count_coeff = all_coefficients["op_count", 1]
  expensive = NA
  
  # there's only one arg coefficient here, silly R forces us to take a special case path...
  has_significant = all_coefficients['op_count:expensiveTRUE', 4] < p_value_thresh

  if (has_significant) {
    coefficient_impact = abs(all_coefficients['op_count:expensiveTRUE', 1])
    has_impacting = has_significant & coefficient_impact > pure_op_count_coeff * impact_ratio
  } else {
    has_impacting = FALSE
  }
  if (has_impacting) {
    expensive = all_coefficients['op_count:expensiveTRUE', 1]
  }
  expensive_raw = all_coefficients['op_count:expensiveTRUE', 1]
  expensive_p = all_coefficients['op_count:expensiveTRUE', 4]
  results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns'] = expensive
  results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns_raw'] = expensive_raw
  results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns_p'] = expensive_p
  return(results_df)
}

# Goes through all the families of OPCODEs and fits and displays their respective `measure_arguments`
# models.
# Results are gathered in a common `results_df` data frame.
analyze_for_env <- function(df, results_df, env) {
  for (opcode in unary_opcodes) {
    model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg0:op_count)
    print(c(opcode, env))
    print(summary(model))
    results_df = add_arg_results(model, opcode, env, results_df, 1)
  }
  for (opcode in binary_opcodes) {
    model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg0:op_count + arg1:op_count)
    print(c(opcode, env))
    print(summary(model))
    results_df = add_arg_results(model, opcode, env, results_df, 2)
  }
  for (opcode in ternary_opcodes) {
    model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg2 + arg0:op_count + arg1:op_count + arg2:op_count)
    print(c(opcode, env))
    print(summary(model))
    results_df = add_arg_results(model, opcode, env, results_df, 3)
  }
  for (opcode in div_opcodes) {
    model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + expensive:op_count)
    print(c(opcode, env))
    print(summary(model))
    results_df = add_arg_expensive_results(model, opcode, env, results_df, 2)
  }
  for (opcode in c('ADDMOD', 'MULMOD')) {
    model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg2 + expensive:op_count)
    print(c(opcode, env))
    print(summary(model))
    results_df = add_arg_expensive_results(model, opcode, env, results_df, 3)
  }
  return(results_df)
}

This is the so-called “first-pass” at the estimation procedure, where we estimated all possible argument impact variables for all OPCODEs. We gather all the results in the first_pass table, inspect this to see where the arguments turned out to be significantly impacting the computation cost.

first_pass = data.frame(matrix(ncol = 17, nrow = 0))
colnames(first_pass) <- c('opcode', 'env', 'has_significant', 'has_impacting', 'estimate_marginal_ns',
                          'arg0_ns', 'arg1_ns', 'arg2_ns', 'expensive_ns',
                          'arg0_ns_raw', 'arg1_ns_raw', 'arg2_ns_raw', 'expensive_ns_raw',
                          'arg0_ns_p', 'arg1_ns_p', 'arg2_ns_p',  'expensive_ns_p')

first_pass = analyze_for_env(measurements, first_pass, env)
## [1] "ISZERO"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1185.12  -325.36   -78.94   339.50  1245.30 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11250.7414    56.5172 199.067 < 0.0000000000000002 ***
## op_count         18.3983     2.8832   6.381       0.000000000357 ***
## arg0             -1.6243     3.1411  -0.517                0.605    
## op_count:arg0     0.1181     0.1607   0.735                0.463    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 428.2 on 587 degrees of freedom
## Multiple R-squared:  0.2521, Adjusted R-squared:  0.2483 
## F-statistic: 65.97 on 3 and 587 DF,  p-value: < 0.00000000000000022
## 
## [1] "NOT"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1062.2  -337.7  -105.2   366.0  1528.9 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   11241.96032    62.21202 180.704 <0.0000000000000002 ***
## op_count         31.34877     3.20970   9.767 <0.0000000000000002 ***
## arg0              1.05525     3.20289   0.329               0.742    
## op_count:arg0     0.05906     0.16566   0.356               0.722    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 449.6 on 584 degrees of freedom
## Multiple R-squared:  0.4393, Adjusted R-squared:  0.4365 
## F-statistic: 152.5 on 3 and 584 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLDATALOAD" "nethermind"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -975.9 -304.6 -105.3  220.4 1228.4 
## 
## Coefficients:
##                     Estimate     Std. Error t value            Pr(>|t|)    
## (Intercept)   13166.02423542    61.76352733 213.168 <0.0000000000000002 ***
## op_count         27.25275273     3.20699014   8.498 <0.0000000000000002 ***
## arg0              0.00004723     0.00617179   0.008               0.994    
## op_count:arg0     0.00010752     0.00031942   0.337               0.737    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 455.1 on 581 degrees of freedom
## Multiple R-squared:  0.3673, Adjusted R-squared:  0.3641 
## F-statistic: 112.4 on 3 and 581 DF,  p-value: < 0.00000000000000022
## 
## [1] "POP"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -695.05 -271.56  -41.15  249.35 1099.26 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   10073.7958    45.3564 222.103 < 0.0000000000000002 ***
## op_count          8.3638     2.3440   3.568             0.000389 ***
## arg0              0.5889     2.3819   0.247             0.804820    
## op_count:arg0    -0.1105     0.1228  -0.899             0.368813    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 343.4 on 590 degrees of freedom
## Multiple R-squared:  0.05359,    Adjusted R-squared:  0.04878 
## F-statistic: 11.14 on 3 and 590 DF,  p-value: 0.0000004052
## 
## [1] "MLOAD"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1088.13  -338.24   -93.68   204.30  1908.72 
## 
## Coefficients:
##                    Estimate    Std. Error t value            Pr(>|t|)    
## (Intercept)   13043.9951626    72.1963916 180.674 <0.0000000000000002 ***
## op_count         73.0671208     3.6900001  19.801 <0.0000000000000002 ***
## arg0              0.0007421     0.0070816   0.105               0.917    
## op_count:arg0    -0.0004458     0.0003628  -1.229               0.220    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 508.7 on 567 degrees of freedom
## Multiple R-squared:  0.7382, Adjusted R-squared:  0.7368 
## F-statistic:   533 on 3 and 567 DF,  p-value: < 0.00000000000000022
## 
## [1] "JUMPI"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2242.3 -1006.2  -325.4   848.9  3848.5 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   12046.5226   168.6941  71.410 <0.0000000000000002 ***
## op_count        174.8372     8.6989  20.099 <0.0000000000000002 ***
## arg0              3.2191     9.1250   0.353               0.724    
## op_count:arg0    -0.4237     0.4700  -0.902               0.368    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1334 on 589 degrees of freedom
## Multiple R-squared:  0.7061, Adjusted R-squared:  0.7046 
## F-statistic: 471.7 on 3 and 589 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP1"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -880.06 -292.44  -73.72  288.48 1207.54 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11310.0280    53.0715 213.109 < 0.0000000000000002 ***
## op_count          7.7732     2.7533   2.823              0.00492 ** 
## arg0             -3.1260     2.7489  -1.137              0.25593    
## op_count:arg0     0.1615     0.1424   1.134              0.25723    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 389.7 on 583 degrees of freedom
## Multiple R-squared:  0.1013, Adjusted R-squared:  0.09663 
## F-statistic: 21.89 on 3 and 583 DF,  p-value: 0.0000000000001902
## 
## [1] "DUP2"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -973.40 -308.76  -55.39  313.34 1338.03 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   11272.21787    50.96969 221.155 < 0.0000000000000002 ***
## op_count         10.22612     2.63486   3.881             0.000116 ***
## arg0              0.24889     2.70779   0.092             0.926797    
## op_count:arg0     0.03859     0.13985   0.276             0.782711    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 403.7 on 592 degrees of freedom
## Multiple R-squared:  0.09898,    Adjusted R-squared:  0.09441 
## F-statistic: 21.68 on 3 and 592 DF,  p-value: 0.000000000000248
## 
## [1] "DUP3"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1124.92  -323.18   -48.73   334.05  1217.30 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   11349.00948    53.41737 212.459 < 0.0000000000000002 ***
## op_count          8.78791     2.75797   3.186              0.00152 ** 
## arg0             -4.06431     2.90259  -1.400              0.16197    
## op_count:arg0     0.04351     0.15103   0.288              0.77338    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 398.8 on 586 degrees of freedom
## Multiple R-squared:  0.08423,    Adjusted R-squared:  0.07954 
## F-statistic: 17.97 on 3 and 586 DF,  p-value: 0.00000000003634
## 
## [1] "DUP4"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1005.34  -298.38   -34.55   311.57  1306.53 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11283.8593    50.9879 221.305 < 0.0000000000000002 ***
## op_count          7.3486     2.6286   2.796              0.00535 ** 
## arg0             -1.8147     2.6914  -0.674              0.50042    
## op_count:arg0     0.2262     0.1391   1.626              0.10441    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 388.1 on 588 degrees of freedom
## Multiple R-squared:  0.1143, Adjusted R-squared:  0.1097 
## F-statistic: 25.28 on 3 and 588 DF,  p-value: 0.000000000000002142
## 
## [1] "DUP5"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -989.86 -311.99  -57.19  326.51 1357.53 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   11303.81551    52.68257 214.565 < 0.0000000000000002 ***
## op_count          9.37518     2.73141   3.432             0.000641 ***
## arg0             -1.20759     2.74764  -0.440             0.660461    
## op_count:arg0     0.06337     0.14213   0.446             0.655856    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 405.5 on 584 degrees of freedom
## Multiple R-squared:  0.09091,    Adjusted R-squared:  0.08624 
## F-statistic: 19.47 on 3 and 584 DF,  p-value: 0.00000000000485
## 
## [1] "DUP6"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -914.38 -366.73  -50.13  336.84 1470.53 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11168.0328    61.7393 180.890 < 0.0000000000000002 ***
## op_count         13.4579     3.1898   4.219            0.0000284 ***
## arg0              5.8747     3.1998   1.836               0.0669 .  
## op_count:arg0    -0.1347     0.1656  -0.814               0.4162    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 438.5 on 591 degrees of freedom
## Multiple R-squared:  0.09494,    Adjusted R-squared:  0.09035 
## F-statistic: 20.67 on 3 and 591 DF,  p-value: 0.0000000000009585
## 
## [1] "DUP7"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1180.25  -321.83   -29.03   302.21  1354.04 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11272.2325    57.9754 194.431 < 0.0000000000000002 ***
## op_count         14.0243     2.9902   4.690            0.0000034 ***
## arg0              4.9186     3.0994   1.587                0.113    
## op_count:arg0    -0.2255     0.1600  -1.410                0.159    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 425.9 on 584 degrees of freedom
## Multiple R-squared:  0.08522,    Adjusted R-squared:  0.08052 
## F-statistic: 18.13 on 3 and 584 DF,  p-value: 0.00000000002908
## 
## [1] "DUP8"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -951.1 -315.4  -41.2  336.1 1342.0 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11289.1622    52.8858 213.463 < 0.0000000000000002 ***
## op_count          9.8743     2.7350   3.610             0.000332 ***
## arg0              2.1848     3.0088   0.726             0.468040    
## op_count:arg0     0.1326     0.1557   0.852             0.394830    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 409.5 on 582 degrees of freedom
## Multiple R-squared:  0.1202, Adjusted R-squared:  0.1156 
## F-statistic:  26.5 on 3 and 582 DF,  p-value: 0.0000000000000004458
## 
## [1] "DUP9"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -930.86 -344.64  -24.66  333.68 1187.14 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11225.0803    57.9886 193.574 < 0.0000000000000002 ***
## op_count         16.1835     2.9924   5.408          0.000000093 ***
## arg0              4.6469     3.1467   1.477                 0.14    
## op_count:arg0    -0.2403     0.1627  -1.477                 0.14    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 427.6 on 583 degrees of freedom
## Multiple R-squared:  0.114,  Adjusted R-squared:  0.1094 
## F-statistic:    25 on 3 and 583 DF,  p-value: 0.000000000000003141
## 
## [1] "DUP10"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -902.07 -304.98  -15.11  317.66 1138.06 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11178.4236    55.2339 202.383 < 0.0000000000000002 ***
## op_count         17.6511     2.8489   6.196         0.0000000011 ***
## arg0              5.3139     2.7400   1.939               0.0529 .  
## op_count:arg0    -0.3041     0.1411  -2.155               0.0316 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 401 on 582 degrees of freedom
## Multiple R-squared:  0.1299, Adjusted R-squared:  0.1254 
## F-statistic: 28.96 on 3 and 582 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP11"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -921.67 -359.89  -86.21  345.37 1554.56 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   11461.4654    62.3178 183.920 <0.0000000000000002 ***
## op_count          7.9161     3.2019   2.472              0.0137 *  
## arg0             -4.3895     3.2490  -1.351              0.1772    
## op_count:arg0     0.1736     0.1673   1.037              0.3000    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 450.7 on 587 degrees of freedom
## Multiple R-squared:  0.08273,    Adjusted R-squared:  0.07804 
## F-statistic: 17.65 on 3 and 587 DF,  p-value: 0.00000000005586
## 
## [1] "DUP12"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -969.24 -317.84  -78.02  315.73 1250.23 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11245.2041    53.0851 211.833 < 0.0000000000000002 ***
## op_count         12.6893     2.7385   4.634           0.00000443 ***
## arg0              1.1859     2.8161   0.421                0.674    
## op_count:arg0    -0.1794     0.1450  -1.238                0.216    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 408.1 on 584 degrees of freedom
## Multiple R-squared:  0.08302,    Adjusted R-squared:  0.07831 
## F-statistic: 17.62 on 3 and 584 DF,  p-value: 0.00000000005791
## 
## [1] "DUP13"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -882.12 -320.86  -60.29  316.23 1242.01 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   11296.77948    59.01261 191.430 < 0.0000000000000002 ***
## op_count         11.15892     3.04624   3.663             0.000272 ***
## arg0             -0.10124     3.03173  -0.033             0.973372    
## op_count:arg0     0.05358     0.15686   0.342             0.732794    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 424.1 on 587 degrees of freedom
## Multiple R-squared:  0.1096, Adjusted R-squared:  0.1051 
## F-statistic: 24.09 on 3 and 587 DF,  p-value: 0.00000000000001024
## 
## [1] "DUP14"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -956.65 -296.14  -72.21  297.99 1135.29 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11267.9226    51.9522 216.890 < 0.0000000000000002 ***
## op_count          9.1456     2.6789   3.414             0.000684 ***
## arg0              0.7929     2.8012   0.283             0.777225    
## op_count:arg0     0.1076     0.1448   0.743             0.457848    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 393.8 on 586 degrees of freedom
## Multiple R-squared:  0.1072, Adjusted R-squared:  0.1026 
## F-statistic: 23.44 on 3 and 586 DF,  p-value: 0.00000000000002422
## 
## [1] "DUP15"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -987.93 -326.02  -80.96  309.75 1482.71 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   11246.0494    60.7845 185.015 <0.0000000000000002 ***
## op_count          8.1210     3.1482   2.580              0.0101 *  
## arg0              0.7066     3.1399   0.225              0.8220    
## op_count:arg0     0.0629     0.1624   0.387              0.6987    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 416.2 on 591 degrees of freedom
## Multiple R-squared:  0.07025,    Adjusted R-squared:  0.06553 
## F-statistic: 14.88 on 3 and 591 DF,  p-value: 0.000000002363
## 
## [1] "DUP16"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -942.63 -339.94  -69.57  314.65 1453.63 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11473.9828    57.4369 199.767 < 0.0000000000000002 ***
## op_count          8.8138     2.9715   2.966              0.00314 ** 
## arg0             -1.8670     2.9969  -0.623              0.53354    
## op_count:arg0     0.1789     0.1554   1.151              0.25024    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 425.3 on 583 degrees of freedom
## Multiple R-squared:  0.1063, Adjusted R-squared:  0.1017 
## F-statistic: 23.11 on 3 and 583 DF,  p-value: 0.00000000000003817
## 
## [1] "ADD"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1002.5  -353.9   -72.4   365.6  1464.2 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   11309.18213    82.83301 136.530 < 0.0000000000000002 ***
## op_count         27.38172     4.32405   6.332       0.000000000483 ***
## arg0             -1.27122     3.27462  -0.388                0.698    
## arg1             -0.95265     3.20664  -0.297                0.767    
## op_count:arg0     0.09481     0.16942   0.560                0.576    
## op_count:arg1     0.14293     0.16568   0.863                0.389    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 453.2 on 582 degrees of freedom
## Multiple R-squared:  0.4227, Adjusted R-squared:  0.4177 
## F-statistic: 85.23 on 5 and 582 DF,  p-value: < 0.00000000000000022
## 
## [1] "MUL"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1118.7  -397.7  -116.3   416.8  1661.5 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   11396.0868    92.6551 122.995 <0.0000000000000002 ***
## op_count         64.3015     4.7852  13.438 <0.0000000000000002 ***
## arg0             -2.6233     3.6555  -0.718              0.4733    
## arg1             -2.0639     3.6951  -0.559              0.5767    
## op_count:arg0     0.1298     0.1896   0.684              0.4941    
## op_count:arg1    -0.3135     0.1901  -1.649              0.0996 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 506.5 on 587 degrees of freedom
## Multiple R-squared:  0.6943, Adjusted R-squared:  0.6917 
## F-statistic: 266.6 on 5 and 587 DF,  p-value: < 0.00000000000000022
## 
## [1] "SUB"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1028.9  -370.6  -105.4   418.7  1416.0 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11274.9161    86.1084 130.939 < 0.0000000000000002 ***
## op_count         27.8737     4.4579   6.253       0.000000000777 ***
## arg0              1.3613     3.4105   0.399                0.690    
## arg1             -0.7169     3.3649  -0.213                0.831    
## op_count:arg0     0.1084     0.1760   0.616                0.538    
## op_count:arg1     0.1138     0.1739   0.654                0.513    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 467 on 587 degrees of freedom
## Multiple R-squared:  0.4102, Adjusted R-squared:  0.4052 
## F-statistic: 81.66 on 5 and 587 DF,  p-value: < 0.00000000000000022
## 
## [1] "DIV"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1472.62  -371.58   -44.52   399.27  2016.13 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11305.4941    94.4679 119.676 < 0.0000000000000002 ***
## op_count         33.3647     4.8759   6.843   0.0000000000195008 ***
## arg0              1.6300     3.7770   0.432             0.666223    
## arg1             -2.7608     3.6940  -0.747             0.455132    
## op_count:arg0     1.4914     0.1950   7.647   0.0000000000000839 ***
## op_count:arg1    -0.6507     0.1909  -3.408             0.000698 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 537.5 on 590 degrees of freedom
## Multiple R-squared:  0.6014, Adjusted R-squared:  0.598 
## F-statistic:   178 on 5 and 590 DF,  p-value: < 0.00000000000000022
## 
## [1] "SDIV"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1611.21  -436.66   -59.41   427.89  2337.53 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11338.5724   106.1898 106.776 < 0.0000000000000002 ***
## op_count         52.1996     5.4538   9.571 < 0.0000000000000002 ***
## arg0             -0.7147     4.3197  -0.165                0.869    
## arg1              2.2316     4.3603   0.512                0.609    
## op_count:arg0     1.9926     0.2218   8.982 < 0.0000000000000002 ***
## op_count:arg1    -1.1711     0.2234  -5.241          0.000000223 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 610.6 on 586 degrees of freedom
## Multiple R-squared:  0.6881, Adjusted R-squared:  0.6855 
## F-statistic: 258.6 on 5 and 586 DF,  p-value: < 0.00000000000000022
## 
## [1] "MOD"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1492.65  -369.67   -20.04   356.69  1467.16 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11187.6090    91.5059 122.261 < 0.0000000000000002 ***
## op_count         38.7728     4.7191   8.216  0.00000000000000136 ***
## arg0              2.0115     3.5883   0.561               0.5753    
## arg1              6.0211     3.6248   1.661               0.0972 .  
## op_count:arg0     1.5284     0.1854   8.246  0.00000000000000109 ***
## op_count:arg1    -1.0541     0.1872  -5.631  0.00000002787512287 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 513 on 585 degrees of freedom
## Multiple R-squared:  0.6379, Adjusted R-squared:  0.6348 
## F-statistic: 206.1 on 5 and 585 DF,  p-value: < 0.00000000000000022
## 
## [1] "SMOD"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1679.22  -440.76   -35.28   410.03  2241.67 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11470.4703   109.5369 104.718 < 0.0000000000000002 ***
## op_count         54.1830     5.6585   9.575 < 0.0000000000000002 ***
## arg0              1.7370     4.2093   0.413                0.680    
## arg1             -5.1664     4.5935  -1.125                0.261    
## op_count:arg0     1.5750     0.2181   7.223     0.00000000000157 ***
## op_count:arg1    -0.5577     0.2370  -2.353                0.019 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 617.2 on 592 degrees of freedom
## Multiple R-squared:  0.6929, Adjusted R-squared:  0.6903 
## F-statistic: 267.2 on 5 and 592 DF,  p-value: < 0.00000000000000022
## 
## [1] "EXP"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -83670  -9345  -3617    627 175645 
## 
## Coefficients:
##                Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   8482.6999  5167.1265   1.642              0.10120    
## op_count       870.2124   265.4011   3.279              0.00111 ** 
## arg0           161.4904   221.0792   0.730              0.46540    
## arg1           195.9630   214.2141   0.915              0.36068    
## op_count:arg0   -0.7686    11.3090  -0.068              0.94584    
## op_count:arg1  274.3254    10.9393  25.077 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 31780 on 576 degrees of freedom
## Multiple R-squared:  0.8779, Adjusted R-squared:  0.8769 
## F-statistic: 828.5 on 5 and 576 DF,  p-value: < 0.00000000000000022
## 
## [1] "SIGNEXTEND" "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -901.43 -306.83  -92.76  313.97 1210.44 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   11363.41359    70.30078 161.640 < 0.0000000000000002 ***
## op_count         11.98944     3.68017   3.258              0.00119 ** 
## arg0             -0.37670     2.81051  -0.134              0.89342    
## arg1             -3.25407     2.90902  -1.119              0.26377    
## op_count:arg0    -0.02723     0.14632  -0.186              0.85246    
## op_count:arg1    -0.07930     0.15117  -0.525              0.60006    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 406.8 on 584 degrees of freedom
## Multiple R-squared:  0.09556,    Adjusted R-squared:  0.08781 
## F-statistic: 12.34 on 5 and 584 DF,  p-value: 0.00000000002148
## 
## [1] "LT"         "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -894.0 -334.6  -92.2  329.0 1541.7 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   11324.61686    82.18303 137.798 < 0.0000000000000002 ***
## op_count         21.77008     4.23100   5.145          0.000000366 ***
## arg0              0.75147     3.19714   0.235                0.814    
## arg1              0.01483     3.29420   0.005                0.996    
## op_count:arg0     0.09613     0.16574   0.580                0.562    
## op_count:arg1    -0.12815     0.17009  -0.753                0.452    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 440.6 on 580 degrees of freedom
## Multiple R-squared:  0.2634, Adjusted R-squared:  0.257 
## F-statistic: 41.48 on 5 and 580 DF,  p-value: < 0.00000000000000022
## 
## [1] "GT"         "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -952.10 -343.30  -83.57  345.04 1323.05 
## 
## Coefficients:
##                   Estimate   Std. Error t value             Pr(>|t|)    
## (Intercept)   11318.470330    77.184650 146.641 < 0.0000000000000002 ***
## op_count         23.564537     3.970908   5.934        0.00000000504 ***
## arg0             -1.798547     3.294410  -0.546                0.585    
## arg1              0.550794     3.144619   0.175                0.861    
## op_count:arg0    -0.021025     0.168513  -0.125                0.901    
## op_count:arg1     0.002458     0.161713   0.015                0.988    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 434.5 on 588 degrees of freedom
## Multiple R-squared:  0.3039, Adjusted R-squared:  0.298 
## F-statistic: 51.35 on 5 and 588 DF,  p-value: < 0.00000000000000022
## 
## [1] "SLT"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -920.36 -368.54  -49.41  373.50 1232.70 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   11283.17122    88.27974 127.812 < 0.0000000000000002 ***
## op_count         36.03767     4.55633   7.909   0.0000000000000129 ***
## arg0             -1.55954     3.31117  -0.471                0.638    
## arg1              0.33807     3.20135   0.106                0.916    
## op_count:arg0    -0.08555     0.17128  -0.500                0.618    
## op_count:arg1     0.01873     0.16551   0.113                0.910    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 464.1 on 588 degrees of freedom
## Multiple R-squared:  0.4614, Adjusted R-squared:  0.4569 
## F-statistic: 100.8 on 5 and 588 DF,  p-value: < 0.00000000000000022
## 
## [1] "SGT"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -891.61 -324.90  -87.55  338.66 1146.08 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11315.4863    75.9850 148.917 < 0.0000000000000002 ***
## op_count         25.9760     3.9388   6.595      0.0000000000952 ***
## arg0              0.5150     3.2517   0.158                0.874    
## arg1             -2.3844     2.9376  -0.812                0.417    
## op_count:arg0     0.1385     0.1678   0.826                0.409    
## op_count:arg1    -0.0267     0.1518  -0.176                0.861    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 423.6 on 586 degrees of freedom
## Multiple R-squared:  0.3968, Adjusted R-squared:  0.3917 
## F-statistic: 77.11 on 5 and 586 DF,  p-value: < 0.00000000000000022
## 
## [1] "EQ"         "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1028.64  -304.38   -78.71   311.29  1115.06 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11289.7822    69.9943 161.296 < 0.0000000000000002 ***
## op_count         19.0458     3.6091   5.277          0.000000185 ***
## arg0              0.7429     2.7952   0.266                0.790    
## arg1             -2.0786     2.9345  -0.708                0.479    
## op_count:arg0    -0.0243     0.1443  -0.168                0.866    
## op_count:arg1     0.1303     0.1511   0.862                0.389    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 400.3 on 586 degrees of freedom
## Multiple R-squared:  0.2917, Adjusted R-squared:  0.2857 
## F-statistic: 48.28 on 5 and 586 DF,  p-value: < 0.00000000000000022
## 
## [1] "AND"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1093.28  -379.86   -87.18   396.83  1487.03 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   11281.0281    81.6042 138.241 <0.0000000000000002 ***
## op_count         39.9884     4.2070   9.505 <0.0000000000000002 ***
## arg0             -3.7612     3.2575  -1.155              0.2487    
## arg1              3.8716     3.5499   1.091              0.2759    
## op_count:arg0     0.3209     0.1681   1.909              0.0567 .  
## op_count:arg1    -0.1420     0.1828  -0.777              0.4376    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 476 on 586 degrees of freedom
## Multiple R-squared:  0.5516, Adjusted R-squared:  0.5478 
## F-statistic: 144.2 on 5 and 586 DF,  p-value: < 0.00000000000000022
## 
## [1] "OR"         "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -982.28 -352.71  -67.83  378.03 1370.94 
## 
## Coefficients:
##                   Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept)   11181.969319    80.396673 139.085 <0.0000000000000002 ***
## op_count         44.799915     4.140538  10.820 <0.0000000000000002 ***
## arg0              4.344679     3.261722   1.332               0.183    
## arg1              0.372761     3.243086   0.115               0.909    
## op_count:arg0    -0.109781     0.167720  -0.655               0.513    
## op_count:arg1     0.004406     0.167360   0.026               0.979    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 451.5 on 586 degrees of freedom
## Multiple R-squared:  0.5807, Adjusted R-squared:  0.5772 
## F-statistic: 162.3 on 5 and 586 DF,  p-value: < 0.00000000000000022
## 
## [1] "XOR"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1114.6  -367.4  -102.5   382.1  1472.0 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   11223.4085    83.2343 134.841 <0.0000000000000002 ***
## op_count         55.3645     4.2857  12.919 <0.0000000000000002 ***
## arg0              2.8407     3.3110   0.858              0.3913    
## arg1              2.1184     3.3164   0.639              0.5232    
## op_count:arg0    -0.4116     0.1705  -2.414              0.0161 *  
## op_count:arg1    -0.3339     0.1710  -1.953              0.0513 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 479.3 on 583 degrees of freedom
## Multiple R-squared:  0.5525, Adjusted R-squared:  0.5486 
## F-statistic: 143.9 on 5 and 583 DF,  p-value: < 0.00000000000000022
## 
## [1] "BYTE"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -874.64 -330.88  -72.75  342.51 1311.48 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   11335.84516    78.50455 144.397 < 0.0000000000000002 ***
## op_count         18.79053     4.04814   4.642           0.00000426 ***
## arg0             -3.23404     3.12731  -1.034                0.302    
## arg1             -0.36478     3.06450  -0.119                0.905    
## op_count:arg0     0.10086     0.16104   0.626                0.531    
## op_count:arg1     0.03258     0.15809   0.206                0.837    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 423.3 on 587 degrees of freedom
## Multiple R-squared:  0.2711, Adjusted R-squared:  0.2649 
## F-statistic: 43.67 on 5 and 587 DF,  p-value: < 0.00000000000000022
## 
## [1] "SHL"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1091.75  -376.29   -25.72   357.92  1512.54 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   11339.98628    80.96564 140.059 < 0.0000000000000002 ***
## op_count         29.47272     4.16889   7.070     0.00000000000445 ***
## arg0             -0.97946     3.20001  -0.306                0.760    
## arg1              0.83671     3.31784   0.252                0.801    
## op_count:arg0    -0.16253     0.16401  -0.991                0.322    
## op_count:arg1     0.05299     0.17084   0.310                0.757    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 458.2 on 584 degrees of freedom
## Multiple R-squared:  0.3578, Adjusted R-squared:  0.3523 
## F-statistic: 65.07 on 5 and 584 DF,  p-value: < 0.00000000000000022
## 
## [1] "SHR"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -989.29 -322.33  -81.34  326.56 1321.30 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11407.5160    71.4784 159.594 < 0.0000000000000002 ***
## op_count         26.9103     3.6591   7.354    0.000000000000655 ***
## arg0              0.6180     3.0806   0.201                0.841    
## arg1             -4.2676     3.0393  -1.404                0.161    
## op_count:arg0    -0.1067     0.1590  -0.671                0.502    
## op_count:arg1     0.0704     0.1564   0.450                0.653    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 431.6 on 582 degrees of freedom
## Multiple R-squared:  0.3672, Adjusted R-squared:  0.3617 
## F-statistic: 67.54 on 5 and 582 DF,  p-value: < 0.00000000000000022
## 
## [1] "SAR"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -901.0 -340.9 -110.2  349.5 1517.6 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   11282.3441    86.3154 130.711 <0.0000000000000002 ***
## op_count         37.5607     4.4220   8.494 <0.0000000000000002 ***
## arg0              2.3783     3.2533   0.731               0.465    
## arg1             -3.0213     3.2616  -0.926               0.355    
## op_count:arg0    -0.1315     0.1676  -0.785               0.433    
## op_count:arg1     0.1341     0.1670   0.803               0.422    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 444.3 on 585 degrees of freedom
## Multiple R-squared:  0.5256, Adjusted R-squared:  0.5215 
## F-statistic: 129.6 on 5 and 585 DF,  p-value: < 0.00000000000000022
## 
## [1] "MSTORE"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -886.81 -305.96  -96.56  214.06 1281.10 
## 
## Coefficients:
##                    Estimate    Std. Error t value            Pr(>|t|)    
## (Intercept)   12095.8387882    80.2051035 150.811 <0.0000000000000002 ***
## op_count         62.0356443     4.1146172  15.077 <0.0000000000000002 ***
## arg0              0.0005848     0.0061545   0.095               0.924    
## arg1              0.0009323     0.0060563   0.154               0.878    
## op_count:arg0    -0.0002570     0.0003197  -0.804               0.422    
## op_count:arg1    -0.0002418     0.0003124  -0.774               0.439    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 445.5 on 581 degrees of freedom
## Multiple R-squared:  0.7189, Adjusted R-squared:  0.7165 
## F-statistic: 297.1 on 5 and 581 DF,  p-value: < 0.00000000000000022
## 
## [1] "MSTORE8"    "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -830.87 -241.50  -33.52  188.65  998.39 
## 
## Coefficients:
##                    Estimate    Std. Error t value            Pr(>|t|)    
## (Intercept)   12062.7563542    61.5499487 195.983 <0.0000000000000002 ***
## op_count         52.6845823     3.2169939  16.377 <0.0000000000000002 ***
## arg0              0.0024697     0.0051255   0.482               0.630    
## arg1              0.0044185     0.0051758   0.854               0.394    
## op_count:arg0    -0.0001703     0.0002669  -0.638               0.524    
## op_count:arg1    -0.0000789     0.0002721  -0.290               0.772    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 355.1 on 554 degrees of freedom
## Multiple R-squared:  0.7528, Adjusted R-squared:  0.7506 
## F-statistic: 337.5 on 5 and 554 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP1"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -774.95 -270.06  -27.02  247.62 1018.85 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   10129.8565    61.0048 166.050 < 0.0000000000000002 ***
## op_count         11.2041     3.1448   3.563             0.000397 ***
## arg0              3.3027     2.4649   1.340             0.180796    
## arg1             -3.2955     2.3939  -1.377             0.169157    
## op_count:arg0    -0.1633     0.1267  -1.289             0.197995    
## op_count:arg1     0.1918     0.1232   1.557             0.119915    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 344.5 on 585 degrees of freedom
## Multiple R-squared:  0.1596, Adjusted R-squared:  0.1525 
## F-statistic: 22.23 on 5 and 585 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP2"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -981.01 -265.96  -46.48  263.82 1180.40 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   10115.36512    64.56883 156.660 < 0.0000000000000002 ***
## op_count         13.60901     3.35938   4.051            0.0000579 ***
## arg0             -1.62842     2.62969  -0.619                0.536    
## arg1              0.27533     2.49282   0.110                0.912    
## op_count:arg0    -0.00645     0.13592  -0.047                0.962    
## op_count:arg1    -0.03866     0.12961  -0.298                0.766    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 358.1 on 582 degrees of freedom
## Multiple R-squared:  0.1653, Adjusted R-squared:  0.1582 
## F-statistic: 23.06 on 5 and 582 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP3"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -904.07 -249.06  -25.81  244.96  982.13 
## 
## Coefficients:
##                   Estimate   Std. Error t value             Pr(>|t|)    
## (Intercept)   10167.727707    58.972965 172.413 < 0.0000000000000002 ***
## op_count         13.430916     3.066697   4.380            0.0000141 ***
## arg0             -1.502049     2.382997  -0.630                0.529    
## arg1             -1.340286     2.312503  -0.580                0.562    
## op_count:arg0    -0.004771     0.123897  -0.039                0.969    
## op_count:arg1    -0.038174     0.119637  -0.319                0.750    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 337.4 on 581 degrees of freedom
## Multiple R-squared:  0.1793, Adjusted R-squared:  0.1722 
## F-statistic: 25.39 on 5 and 581 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP4"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -935.14 -258.38  -48.97  247.13  997.73 
## 
## Coefficients:
##                   Estimate   Std. Error t value             Pr(>|t|)    
## (Intercept)   10042.176303    61.464721 163.381 < 0.0000000000000002 ***
## op_count         13.756608     3.165897   4.345            0.0000164 ***
## arg0             -0.711194     2.496743  -0.285                0.776    
## arg1              3.082594     2.393842   1.288                0.198    
## op_count:arg0    -0.056723     0.129031  -0.440                0.660    
## op_count:arg1    -0.003359     0.122852  -0.027                0.978    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 339.6 on 580 degrees of freedom
## Multiple R-squared:  0.1825, Adjusted R-squared:  0.1754 
## F-statistic: 25.89 on 5 and 580 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP5"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -997.38 -257.02  -47.21  260.23  983.63 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   10185.9608    66.4004 153.402 <0.0000000000000002 ***
## op_count          7.9710     3.4360   2.320              0.0207 *  
## arg0             -1.6646     2.4658  -0.675              0.4999    
## arg1             -3.1864     2.6590  -1.198              0.2313    
## op_count:arg0     0.1354     0.1276   1.061              0.2891    
## op_count:arg1     0.1103     0.1382   0.798              0.4250    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 350.2 on 583 degrees of freedom
## Multiple R-squared:  0.1535, Adjusted R-squared:  0.1462 
## F-statistic: 21.14 on 5 and 583 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP6"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -855.48 -242.78  -46.44  241.96 1034.26 
## 
## Coefficients:
##                Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   9997.7908    63.7740 156.769 < 0.0000000000000002 ***
## op_count        16.8105     3.2877   5.113          0.000000429 ***
## arg0             3.9384     2.4200   1.627               0.1042    
## arg1             4.2332     2.4106   1.756               0.0796 .  
## op_count:arg0   -0.1292     0.1250  -1.034               0.3015    
## op_count:arg1   -0.1297     0.1241  -1.046               0.2962    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 340.6 on 587 degrees of freedom
## Multiple R-squared:  0.1737, Adjusted R-squared:  0.1667 
## F-statistic: 24.68 on 5 and 587 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP7"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -824.61 -253.89  -43.59  227.54 1024.31 
## 
## Coefficients:
##                   Estimate   Std. Error t value             Pr(>|t|)    
## (Intercept)   10081.400990    65.169336 154.695 < 0.0000000000000002 ***
## op_count         14.418803     3.332097   4.327            0.0000178 ***
## arg0              0.521516     2.584877   0.202               0.8402    
## arg1              4.959590     2.654395   1.868               0.0622 .  
## op_count:arg0    -0.084922     0.133009  -0.638               0.5234    
## op_count:arg1    -0.004248     0.136967  -0.031               0.9753    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 347.3 on 582 degrees of freedom
## Multiple R-squared:  0.1824, Adjusted R-squared:  0.1754 
## F-statistic: 25.97 on 5 and 582 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP8"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -810.28 -265.12  -21.86  258.73 1068.56 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   10034.99334    59.74443 167.965 < 0.0000000000000002 ***
## op_count         16.82995     3.09237   5.442         0.0000000775 ***
## arg0              1.66934     2.49053   0.670                0.503    
## arg1              1.80160     2.53837   0.710                0.478    
## op_count:arg0    -0.05623     0.12812  -0.439                0.661    
## op_count:arg1    -0.08260     0.13036  -0.634                0.527    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 350.7 on 583 degrees of freedom
## Multiple R-squared:  0.2101, Adjusted R-squared:  0.2034 
## F-statistic: 31.02 on 5 and 583 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP9"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1011.76  -256.88   -30.97   283.73  1092.88 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   10136.7932    69.5755 145.695 < 0.0000000000000002 ***
## op_count         12.4195     3.5927   3.457             0.000586 ***
## arg0              1.6596     2.7156   0.611             0.541339    
## arg1             -3.7389     2.5731  -1.453             0.146729    
## op_count:arg0    -0.1308     0.1403  -0.932             0.351559    
## op_count:arg1     0.2010     0.1328   1.513             0.130768    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 362.3 on 587 degrees of freedom
## Multiple R-squared:  0.1814, Adjusted R-squared:  0.1744 
## F-statistic: 26.02 on 5 and 587 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP10"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -789.60 -285.42  -47.74  279.61 1084.40 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   10235.48653    67.53770 151.552 < 0.0000000000000002 ***
## op_count         13.79552     3.47281   3.972              0.00008 ***
## arg0             -0.04736     2.46626  -0.019                0.985    
## arg1             -1.61342     2.67436  -0.603                0.547    
## op_count:arg0    -0.01384     0.12679  -0.109                0.913    
## op_count:arg1     0.06652     0.13812   0.482                0.630    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 357.7 on 585 degrees of freedom
## Multiple R-squared:  0.2044, Adjusted R-squared:  0.1976 
## F-statistic: 30.05 on 5 and 585 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP11"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -668.06 -277.08  -52.53  263.14 1102.83 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   10115.91694    61.95567 163.277 < 0.0000000000000002 ***
## op_count          9.59124     3.19922   2.998              0.00283 ** 
## arg0             -1.15298     2.62945  -0.438              0.66119    
## arg1             -0.83567     2.57348  -0.325              0.74551    
## op_count:arg0     0.18631     0.13509   1.379              0.16837    
## op_count:arg1     0.08239     0.13268   0.621              0.53486    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 358.1 on 585 degrees of freedom
## Multiple R-squared:  0.1875, Adjusted R-squared:  0.1805 
## F-statistic: 26.99 on 5 and 585 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP12"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -931.19 -277.34  -51.49  268.60 1105.60 
## 
## Coefficients:
##                   Estimate   Std. Error t value             Pr(>|t|)    
## (Intercept)   10141.573281    61.525101 164.836 < 0.0000000000000002 ***
## op_count         14.308453     3.176435   4.505           0.00000804 ***
## arg0              1.444139     2.513974   0.574                0.566    
## arg1             -1.731997     2.580525  -0.671                0.502    
## op_count:arg0     0.011840     0.130348   0.091                0.928    
## op_count:arg1    -0.009941     0.133569  -0.074                0.941    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 366.3 on 583 degrees of freedom
## Multiple R-squared:  0.1906, Adjusted R-squared:  0.1836 
## F-statistic: 27.45 on 5 and 583 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP13"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -770.99 -262.76  -30.69  244.85 1068.45 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   10131.4168    58.6486 172.748 < 0.0000000000000002 ***
## op_count         13.3928     3.0432   4.401            0.0000128 ***
## arg0             -1.1950     2.4542  -0.487                0.627    
## arg1              3.3428     2.4219   1.380                0.168    
## op_count:arg0     0.0287     0.1274   0.225                0.822    
## op_count:arg1    -0.1344     0.1262  -1.065                0.288    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 347.9 on 581 degrees of freedom
## Multiple R-squared:  0.1486, Adjusted R-squared:  0.1413 
## F-statistic: 20.28 on 5 and 581 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP14"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1038.32  -283.99   -39.12   264.98  1192.13 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   10099.1435    63.8292 158.221 < 0.0000000000000002 ***
## op_count         11.6296     3.2886   3.536             0.000438 ***
## arg0              3.6572     2.7506   1.330             0.184170    
## arg1             -2.3028     2.6105  -0.882             0.378074    
## op_count:arg0    -0.1143     0.1418  -0.806             0.420516    
## op_count:arg1     0.1508     0.1345   1.121             0.262849    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 370.2 on 587 degrees of freedom
## Multiple R-squared:  0.148,  Adjusted R-squared:  0.1408 
## F-statistic:  20.4 on 5 and 587 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP15"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -741.25 -290.03  -51.24  268.38 1126.03 
## 
## Coefficients:
##                  Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)   10388.72159    66.74900 155.639 <0.0000000000000002 ***
## op_count          8.73186     3.46114   2.523              0.0119 *  
## arg0             -2.38781     2.58020  -0.925              0.3551    
## arg1             -3.22225     2.57789  -1.250              0.2118    
## op_count:arg0     0.14752     0.13346   1.105              0.2695    
## op_count:arg1     0.03426     0.13383   0.256              0.7980    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 366.5 on 584 degrees of freedom
## Multiple R-squared:  0.1405, Adjusted R-squared:  0.1332 
## F-statistic:  19.1 on 5 and 584 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP16"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -899.34 -276.99  -42.58  262.74 1062.05 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   10133.17584    65.22558 155.356 < 0.0000000000000002 ***
## op_count         17.06940     3.37669   5.055          0.000000575 ***
## arg0              2.91406     2.47734   1.176                0.240    
## arg1              2.58108     2.67459   0.965                0.335    
## op_count:arg0    -0.19021     0.12860  -1.479                0.140    
## op_count:arg1    -0.06497     0.13878  -0.468                0.640    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 367.4 on 588 degrees of freedom
## Multiple R-squared:  0.1631, Adjusted R-squared:  0.156 
## F-statistic: 22.92 on 5 and 588 DF,  p-value: < 0.00000000000000022
## 
## [1] "ADDMOD"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1925.41  -417.91   -73.87   455.65  2635.90 
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   11178.9290   141.4850  79.011 < 0.0000000000000002 ***
## op_count         60.6174     7.3035   8.300 0.000000000000000715 ***
## arg0              2.9558     4.2682   0.693             0.488881    
## arg1              5.4713     4.4222   1.237             0.216494    
## arg2             -3.5482     4.6692  -0.760             0.447614    
## op_count:arg0     0.6398     0.2199   2.910             0.003750 ** 
## op_count:arg1     0.8601     0.2285   3.765             0.000183 ***
## op_count:arg2    -0.1962     0.2415  -0.812             0.416922    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 628 on 589 degrees of freedom
## Multiple R-squared:  0.7404, Adjusted R-squared:  0.7374 
## F-statistic:   240 on 7 and 589 DF,  p-value: < 0.00000000000000022
## 
## [1] "MULMOD"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2931.7  -576.6  -111.0   605.4  4025.7 
## 
## Coefficients:
##                  Estimate  Std. Error t value             Pr(>|t|)    
## (Intercept)   11260.96110   185.47167  60.715 < 0.0000000000000002 ***
## op_count        134.02309     9.54062  14.048 < 0.0000000000000002 ***
## arg0              4.73408     6.08548   0.778             0.436924    
## arg1              6.35487     6.29910   1.009             0.313461    
## arg2             -1.57008     6.16816  -0.255             0.799163    
## op_count:arg0     1.19757     0.31318   3.824             0.000145 ***
## op_count:arg1     1.51056     0.32509   4.647           0.00000417 ***
## op_count:arg2     0.06493     0.31881   0.204             0.838679    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 900.7 on 586 degrees of freedom
## Multiple R-squared:  0.8609, Adjusted R-squared:  0.8593 
## F-statistic: 518.2 on 7 and 586 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLDATACOPY" "nethermind"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1066.5  -372.9  -101.8   177.5  2206.7 
## 
## Coefficients:
##                    Estimate    Std. Error t value            Pr(>|t|)    
## (Intercept)   12109.7662042   113.5654203 106.633 <0.0000000000000002 ***
## op_count        121.9291536     5.8569415  20.818 <0.0000000000000002 ***
## arg0              0.0021305     0.0079602   0.268               0.789    
## arg1             -0.0021722     0.0082100  -0.265               0.791    
## arg2             -0.0018819     0.0075485  -0.249               0.803    
## op_count:arg0    -0.0001911     0.0004117  -0.464               0.643    
## op_count:arg1     0.0004553     0.0004237   1.075               0.283    
## op_count:arg2     0.0033309     0.0003912   8.515 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 570.6 on 577 degrees of freedom
## Multiple R-squared:  0.9164, Adjusted R-squared:  0.9154 
## F-statistic: 903.8 on 7 and 577 DF,  p-value: < 0.00000000000000022
## 
## [1] "CODECOPY"   "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1527.8  -500.5  -145.6   451.8  2099.3 
## 
## Coefficients:
##                    Estimate    Std. Error t value             Pr(>|t|)    
## (Intercept)   12154.9512929   157.8618622  76.997 < 0.0000000000000002 ***
## op_count        148.3869388     8.1626820  18.179 < 0.0000000000000002 ***
## arg0             -0.0053936     0.0101918  -0.529                0.597    
## arg1             -0.0015672     0.0100409  -0.156                0.876    
## arg2              0.0051780     0.0102343   0.506                0.613    
## op_count:arg0     0.0005368     0.0005271   1.018                0.309    
## op_count:arg1    -0.0023355     0.0005228  -4.468          0.000009512 ***
## op_count:arg2     0.0026838     0.0005290   5.073          0.000000528 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 721.6 on 579 degrees of freedom
## Multiple R-squared:  0.8787, Adjusted R-squared:  0.8772 
## F-statistic:   599 on 7 and 579 DF,  p-value: < 0.00000000000000022
## 
## [1] "RETURNDATACOPY" "nethermind"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2749.6  -643.9  -217.0   686.0  3947.2 
## 
## Coefficients:
##                    Estimate    Std. Error t value            Pr(>|t|)    
## (Intercept)   21122.4488465   176.9122385 119.395 <0.0000000000000002 ***
## op_count        144.9330760     9.0938990  15.937 <0.0000000000000002 ***
## arg0              0.0002961     0.0138644   0.021               0.983    
## arg1             -0.0001804     0.0127753  -0.014               0.989    
## arg2             -0.0070687     0.0124295  -0.569               0.570    
## op_count:arg0    -0.0002008     0.0007140  -0.281               0.779    
## op_count:arg1    -0.0002799     0.0006579  -0.425               0.671    
## op_count:arg2     0.0063981     0.0006410   9.982 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 925.4 on 583 degrees of freedom
## Multiple R-squared:  0.8746, Adjusted R-squared:  0.8731 
## F-statistic:   581 on 7 and 583 DF,  p-value: < 0.00000000000000022
## 
## [1] "DIV"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1426.03  -374.64   -67.92   377.02  2074.51 
## 
## Coefficients:
##                         Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            11116.986     61.303 181.345 < 0.0000000000000002 ***
## op_count                  29.712      2.162  13.741 < 0.0000000000000002 ***
## arg0                       6.994      2.594   2.696              0.00722 ** 
## arg1                       3.277      2.505   1.308              0.19129    
## op_count:expensiveTRUE    35.415      2.753  12.866 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 502.3 on 591 degrees of freedom
## Multiple R-squared:  0.6512, Adjusted R-squared:  0.6489 
## F-statistic: 275.9 on 4 and 591 DF,  p-value: < 0.00000000000000022
## 
## [1] "MOD"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1159.66  -366.80   -66.59   389.35  1332.15 
## 
## Coefficients:
##                         Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            11071.627     60.015 184.482 < 0.0000000000000002 ***
## op_count                  29.161      2.162  13.489 < 0.0000000000000002 ***
## arg0                       7.624      2.498   3.053              0.00237 ** 
## arg1                       7.202      2.504   2.876              0.00418 ** 
## op_count:expensiveTRUE    36.304      2.685  13.521 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 484.1 on 586 degrees of freedom
## Multiple R-squared:  0.677,  Adjusted R-squared:  0.6748 
## F-statistic:   307 on 4 and 586 DF,  p-value: < 0.00000000000000022
## 
## [1] "SDIV"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1615.51  -439.84   -76.37   409.29  2831.57 
## 
## Coefficients:
##                         Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)            11160.941     69.666 160.207 <0.0000000000000002 ***
## op_count                  44.396      2.523  17.598 <0.0000000000000002 ***
## arg0                       7.248      3.045   2.380              0.0176 *  
## arg1                       4.872      2.997   1.626              0.1046    
## op_count:expensiveTRUE    43.153      3.184  13.555 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 576.9 on 587 degrees of freedom
## Multiple R-squared:  0.7212, Adjusted R-squared:  0.7193 
## F-statistic: 379.6 on 4 and 587 DF,  p-value: < 0.00000000000000022
## 
## [1] "SMOD"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2121.2  -447.5     3.9   421.7  2214.2 
## 
## Coefficients:
##                         Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)            11252.369     70.804 158.922 <0.0000000000000002 ***
## op_count                  52.326      2.406  21.752 <0.0000000000000002 ***
## arg0                       6.386      2.911   2.194              0.0286 *  
## arg1                       3.804      3.043   1.250              0.2117    
## op_count:expensiveTRUE    39.268      3.147  12.477 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 575.7 on 593 degrees of freedom
## Multiple R-squared:  0.7324, Adjusted R-squared:  0.7306 
## F-statistic: 405.7 on 4 and 593 DF,  p-value: < 0.00000000000000022
## 
## [1] "ADDMOD"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1755.1  -432.0   -51.2   416.0  3256.6 
## 
## Coefficients:
##                         Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)            10929.062     87.857 124.396 <0.0000000000000002 ***
## op_count                  54.751      3.171  17.268 <0.0000000000000002 ***
## arg0                       3.454      2.605   1.326              0.1854    
## arg1                       8.878      2.707   3.279              0.0011 ** 
## arg2                       7.571      2.984   2.537              0.0114 *  
## op_count:expensiveTRUE    38.587      3.367  11.462 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 578.8 on 591 degrees of freedom
## Multiple R-squared:  0.7788, Adjusted R-squared:  0.7769 
## F-statistic:   416 on 5 and 591 DF,  p-value: < 0.00000000000000022
## 
## [1] "MULMOD"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2006.8  -584.5  -110.8   655.5  3515.8 
## 
## Coefficients:
##                         Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            10726.418    121.670  88.160 < 0.0000000000000002 ***
## op_count                 137.089      5.468  25.073 < 0.0000000000000002 ***
## arg0                      10.483      3.927   2.669             0.007810 ** 
## arg1                      18.468      3.996   4.621           0.00000469 ***
## arg2                      13.793      4.070   3.389             0.000748 ***
## op_count:expensiveTRUE    51.383      5.644   9.104 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 866.7 on 588 degrees of freedom
## Multiple R-squared:  0.8708, Adjusted R-squared:  0.8697 
## F-statistic: 792.5 on 5 and 588 DF,  p-value: < 0.00000000000000022
proceed_with_opcodes = unique(first_pass[which(first_pass$has_impacting == 'TRUE'), 'opcode'])

models_with_args_automatic = first_pass[which(first_pass$has_impacting == 'TRUE'), c('opcode', 'env')]
models_with_expensive_automatic = first_pass[which(!is.na(first_pass$expensive_ns)), c('opcode', 'env')]

first_pass[which(first_pass$has_impacting == 'TRUE'), ]
##    opcode        env has_significant has_impacting estimate_marginal_ns arg0_ns
## 30    EXP nethermind            TRUE          TRUE     870.212425320273    <NA>
##             arg1_ns arg2_ns expensive_ns        arg0_ns_raw      arg1_ns_raw
## 30 274.325423327779    <NA>         <NA> -0.768641020506283 274.325423327779
##    arg2_ns_raw expensive_ns_raw         arg0_ns_p
## 30        <NA>             <NA> 0.945835425750283
##                                                                                                         arg1_ns_p
## 30 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000226222424685711
##    arg2_ns_p expensive_ns_p
## 30      <NA>           <NA>

We inspect the automatic choice of models, but then coerce the choice to a fixed list. We drop the division OPCODEs (DIV etc.), because their arguments only seem to have an indirect impact via the fact that x / y is trivial if x < y. This makes the DIV(x, y) appear costlier for large x and cheaper for large y.

models_with_args = data.frame(opcode="EXP", env=env, arg=1)
first_pass$arg1_ns[is.na(first_pass$arg1_ns) & first_pass$opcode=="EXP" & first_pass$env==env] <- first_pass$arg1_ns_raw[is.na(first_pass$arg1_ns) & first_pass$opcode=="EXP" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="CALLDATACOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="CALLDATACOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="CALLDATACOPY" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="CODECOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="CODECOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="CODECOPY" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="RETURNDATACOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="RETURNDATACOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="RETURNDATACOPY" & first_pass$env==env]

models_with_expensive = data.frame(opcode="DIV", env=env)
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="SDIV", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="MOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="SMOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="ADDMOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="MULMOD", env=env))

Detailed analysis for selected OPCODEs

We go through all the OPCODEs which turned out to have impacting arguments in the automatic discrimination procedure, and we plot some validation plots to inspect these relationships.

# Takes the results data frame and checks which argument indices (0, 1, etc.)
# turned out to be impacting
get_impact_args_for <- function(df, opcode, env) {
  if (opcode %in% nullary_opcodes) {
    return(c())
  }
  args = c()
  for (n in 0:2) {
    argname = paste0('arg', n, '_ns')
    if (!is.na(df[which(df$opcode==opcode & df$env==env), argname])) {
      args = c(n, args)
    }
  }
  return(rev(args))
}

# same as `get_impact_args_for` but gets all the argument indices
get_args_for <- function(df, opcode, env) {
  if (opcode %in% unary_opcodes) {
    c(0)
  } else if (opcode %in% binary_opcodes) {
    c(0, 1)
  } else if (opcode %in% ternary_opcodes) {
    c(0, 1, 2)
  }
}

# Builds a final model formula to estimate, based on whether the arguments
# came out impactful from the automatic discrimination process.
get_model_formula_for <- function(df, opcode, env) {
  args = get_args_for(df, opcode, env)
  argnames = paste0('arg', args)
  args_formula = paste0(argnames, collapse=' + ')
  
  impact_args = get_impact_args_for(df, opcode, env)
  if (opcode %in% nullary_opcodes) {
    as.formula('measure_total_time_ns ~ op_count')
  } else if (is.null(impact_args)) {
    as.formula(paste0('measure_total_time_ns ~ op_count +  ', args_formula))
  } else {
  arg_op_count_names = paste0('arg', impact_args, ':op_count')
  arg_op_counts_formula = paste0(arg_op_count_names, collapse=' + ')
  as.formula(paste0('measure_total_time_ns ~ op_count +  ', args_formula, ' + ', arg_op_counts_formula))
  }
}

# Same as `get_model_formula_for` but gauged towards the division OPCODEs specifically.
get_expensive_model_formula_for <- function(df, opcode, env) {
  args = get_args_for(df, opcode, env)
  argnames = paste0('arg', args)
  args_formula = paste0(argnames, collapse=' + ')
  as.formula(paste0('measure_total_time_ns ~ op_count +  ', args_formula, ' + expensive:op_count'))
}

# Same as `get_model_formula_for` but returns the formula to provide the `aggregate` function with.
get_aggregate_formula_for <- function(df, opcode, env) {
  args = get_args_for(df, opcode, env)
  argnames = paste0('arg', args)
  args_formula = paste0(argnames, collapse=' * ')
  as.formula(paste0('measure_total_time_ns ~ op_count * env * opcode * ', args_formula))
}

# Presents the diagnostic plots for a given slice of the data
plot_model <- function(df, opcode, env, use_mean) {
  if (missing(use_mean)) {
    use_mean = FALSE
  }
  if (use_mean) {
    df = aggregate(get_aggregate_formula_for(df, opcode, env), measurements[which(df$opcode==opcode & df$env==env), ], mean, na.action=na.pass)
  }
  model = arg_lm(df, opcode, env, get_model_formula_for(first_pass, opcode, env))
  print(c(opcode, env))
  print(summary(model))
  
  par(mfrow=c(2,2))
  plot(model)
  
  plot_data = df[which(df$env == env & df$opcode == opcode & df$op_count == max(df$op_count)), ]
  if (opcode %in% binary_opcodes) {
    par(mfrow=c(1,1))
    
    decreasing_colors = heat.colors(nrow(plot_data))
    plot_data=plot_data[order(plot_data$measure_total_time_ns, decreasing=TRUE), ]
    with(plot_data, plot(arg0, arg1, col=decreasing_colors, pch=19))
  }
  title(main=paste(opcode, env))
}

Using the functions defined above, we proceed to plot the diagnostic plots of the arguments models.

for (env in all_envs) {
  for (opcode in proceed_with_opcodes) {
    plot_model(measurements, opcode, env, use_mean=TRUE)
  } 
}
## [1] "EXP"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -80670  -8378  -3498    715 175313 
## 
## Coefficients:
##               Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    8975.14    4628.14   1.939                0.053 .  
## op_count        950.88     210.95   4.508           0.00000814 ***
## arg0             89.25     144.22   0.619                0.536    
## arg1            220.28     219.59   1.003                0.316    
## op_count:arg1   268.89      11.20  24.015 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 30600 on 511 degrees of freedom
## Multiple R-squared:  0.8841, Adjusted R-squared:  0.8832 
## F-statistic: 974.6 on 4 and 511 DF,  p-value: < 0.00000000000000022

Producing the final estimates

We’d like to only estimate using the arg-variables in models, where this actually matters to avoid spurious impact of insignificant variables.

We’ll estimate a model with only those argument variables, where they turned out impacting. For those where no argument variable was impacting, we’ll only estimate the marginal increase (corresponding to the constant cost of an OPCODE).

# `results_df` is assumed to have the columns as the `estimates` data frame has (see below)
add_non_arg_model_estimates <- function(model, results_df, env, opcode) {
  pure_op_count_coeff = summary(model)$coefficients["op_count", 1]
  args_ns = c(NA, NA, NA)
  args_ns_stderr = c(NA, NA, NA)
  results_df[nrow(results_df) + 1, ] = c(opcode, env, FALSE, FALSE, pure_op_count_coeff, args_ns, NA, args_ns_stderr, NA)
  return(results_df)
}
add_arg_model_estimates <- function(model, opcode, env, results_df, df) {
  all_coefficients = summary(model)$coefficients
  arg_coefficients = all_coefficients[!(row.names(all_coefficients) %in% c("op_count", "(Intercept)", "arg0", "arg1", "arg2")),]
  pure_op_count_coeff = all_coefficients["op_count", 1]
  # will be filled if any is impacting
  args_ns = c(NA, NA, NA)
  args_ns_stderr = c(NA, NA, NA)
  
  impact_args = get_impact_args_for(df, opcode, env)
  arg_op_count_names = paste0('op_count:arg', impact_args)

  args_ns[impact_args + 1] = all_coefficients[arg_op_count_names, 'Estimate']
  args_ns_stderr[impact_args + 1] = all_coefficients[arg_op_count_names, 'Std. Error']
  results_df[nrow(results_df) + 1, ] = c(opcode, env, TRUE, TRUE, pure_op_count_coeff, args_ns, NA, args_ns_stderr, NA)
  return(results_df)
}
add_expensive_model_estimates <- function(model, opcode, env, results_df, df) {
  all_coefficients = summary(model)$coefficients
  pure_op_count_coeff = all_coefficients["op_count", 1]
  args_ns = c(NA, NA, NA)
  args_ns_stderr = c(NA, NA, NA)
  expensive =  all_coefficients['op_count:expensiveTRUE', 'Estimate']
  expensive_stderr = all_coefficients['op_count:expensiveTRUE', 'Std. Error']
  results_df[nrow(results_df) + 1, ] = c(opcode, env, TRUE, TRUE, pure_op_count_coeff, args_ns, expensive, args_ns_stderr, expensive_stderr)
  return(results_df)
}
estimates = data.frame(matrix(ncol = 13, nrow = 0))
colnames(estimates) <- c('opcode', 'env', 'has_significant', 'has_impacting', 'estimate_marginal_ns',
                         'arg0_ns', 'arg1_ns', 'arg2_ns', 'expensive_ns', 'arg0_ns_stderr', 'arg1_ns_stderr', 'arg2_ns_stderr', 'expensive_ns_stderr')

for (env in all_envs) {
  for (opcode in all_opcodes) {
    is_modeled_with_args = nrow(merge(data.frame(opcode=opcode, env=env), models_with_args)) > 0
    is_modeled_with_expensive = nrow(merge(data.frame(opcode=opcode, env=env), models_with_expensive)) > 0
    if (is_modeled_with_expensive) {
      model = arg_lm(measurements, opcode, env, get_expensive_model_formula_for(first_pass, opcode, env))
      estimates = add_expensive_model_estimates(model, opcode, env, estimates, first_pass)
    } else if (is_modeled_with_args) {
      model = arg_lm(measurements, opcode, env, get_model_formula_for(first_pass, opcode, env))
      estimates = add_arg_model_estimates(model, opcode, env, estimates, first_pass)
    } else {
      model = arg_lm(measurements, opcode, env, get_model_formula_for(first_pass, opcode, env))
      estimates = add_non_arg_model_estimates(model, estimates, env, opcode)
    }
    print(c(opcode, env))
    print(summary(model))
  }
}
## [1] "ADD"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1013.06  -354.64   -72.61   356.52  1496.91 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11249.0211    57.2431 196.513 <0.0000000000000002 ***
## op_count       31.4480     1.5249  20.623 <0.0000000000000002 ***
## arg0            0.1237     2.0729   0.060               0.952    
## arg1            1.1768     2.0249   0.581               0.561    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 452.9 on 584 degrees of freedom
## Multiple R-squared:  0.4216, Adjusted R-squared:  0.4187 
## F-statistic: 141.9 on 3 and 584 DF,  p-value: < 0.00000000000000022
## 
## [1] "MUL"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1191.5  -406.2  -103.0   414.8  1661.2 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 11434.540     64.007 178.646 < 0.0000000000000002 ***
## op_count       61.779      1.701  36.311 < 0.0000000000000002 ***
## arg0           -0.695      2.323  -0.299              0.76494    
## arg1           -6.802      2.329  -2.920              0.00363 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 507.2 on 589 degrees of freedom
## Multiple R-squared:  0.6925, Adjusted R-squared:  0.6909 
## F-statistic: 442.1 on 3 and 589 DF,  p-value: < 0.00000000000000022
## 
## [1] "SUB"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1028.54  -366.16   -94.46   416.42  1415.87 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11220.6278    59.2215 189.469 <0.0000000000000002 ***
## op_count       31.5027     1.5629  20.156 <0.0000000000000002 ***
## arg0            2.9867     2.1507   1.389               0.165    
## arg1            0.9828     2.1295   0.462               0.645    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 466.5 on 589 degrees of freedom
## Multiple R-squared:  0.4095, Adjusted R-squared:  0.4065 
## F-statistic: 136.1 on 3 and 589 DF,  p-value: < 0.00000000000000022
## 
## [1] "DIV"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1426.03  -374.64   -67.92   377.02  2074.51 
## 
## Coefficients:
##                         Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            11116.986     61.303 181.345 < 0.0000000000000002 ***
## op_count                  29.712      2.162  13.741 < 0.0000000000000002 ***
## arg0                       6.994      2.594   2.696              0.00722 ** 
## arg1                       3.277      2.505   1.308              0.19129    
## op_count:expensiveTRUE    35.415      2.753  12.866 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 502.3 on 591 degrees of freedom
## Multiple R-squared:  0.6512, Adjusted R-squared:  0.6489 
## F-statistic: 275.9 on 4 and 591 DF,  p-value: < 0.00000000000000022
## 
## [1] "SDIV"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1615.51  -439.84   -76.37   409.29  2831.57 
## 
## Coefficients:
##                         Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)            11160.941     69.666 160.207 <0.0000000000000002 ***
## op_count                  44.396      2.523  17.598 <0.0000000000000002 ***
## arg0                       7.248      3.045   2.380              0.0176 *  
## arg1                       4.872      2.997   1.626              0.1046    
## op_count:expensiveTRUE    43.153      3.184  13.555 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 576.9 on 587 degrees of freedom
## Multiple R-squared:  0.7212, Adjusted R-squared:  0.7193 
## F-statistic: 379.6 on 4 and 587 DF,  p-value: < 0.00000000000000022
## 
## [1] "MOD"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1159.66  -366.80   -66.59   389.35  1332.15 
## 
## Coefficients:
##                         Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            11071.627     60.015 184.482 < 0.0000000000000002 ***
## op_count                  29.161      2.162  13.489 < 0.0000000000000002 ***
## arg0                       7.624      2.498   3.053              0.00237 ** 
## arg1                       7.202      2.504   2.876              0.00418 ** 
## op_count:expensiveTRUE    36.304      2.685  13.521 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 484.1 on 586 degrees of freedom
## Multiple R-squared:  0.677,  Adjusted R-squared:  0.6748 
## F-statistic:   307 on 4 and 586 DF,  p-value: < 0.00000000000000022
## 
## [1] "SMOD"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2121.2  -447.5     3.9   421.7  2214.2 
## 
## Coefficients:
##                         Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)            11252.369     70.804 158.922 <0.0000000000000002 ***
## op_count                  52.326      2.406  21.752 <0.0000000000000002 ***
## arg0                       6.386      2.911   2.194              0.0286 *  
## arg1                       3.804      3.043   1.250              0.2117    
## op_count:expensiveTRUE    39.268      3.147  12.477 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 575.7 on 593 degrees of freedom
## Multiple R-squared:  0.7324, Adjusted R-squared:  0.7306 
## F-statistic: 405.7 on 4 and 593 DF,  p-value: < 0.00000000000000022
## 
## [1] "ADDMOD"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1755.1  -432.0   -51.2   416.0  3256.6 
## 
## Coefficients:
##                         Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)            10929.062     87.857 124.396 <0.0000000000000002 ***
## op_count                  54.751      3.171  17.268 <0.0000000000000002 ***
## arg0                       3.454      2.605   1.326              0.1854    
## arg1                       8.878      2.707   3.279              0.0011 ** 
## arg2                       7.571      2.984   2.537              0.0114 *  
## op_count:expensiveTRUE    38.587      3.367  11.462 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 578.8 on 591 degrees of freedom
## Multiple R-squared:  0.7788, Adjusted R-squared:  0.7769 
## F-statistic:   416 on 5 and 591 DF,  p-value: < 0.00000000000000022
## 
## [1] "MULMOD"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2006.8  -584.5  -110.8   655.5  3515.8 
## 
## Coefficients:
##                         Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            10726.418    121.670  88.160 < 0.0000000000000002 ***
## op_count                 137.089      5.468  25.073 < 0.0000000000000002 ***
## arg0                      10.483      3.927   2.669             0.007810 ** 
## arg1                      18.468      3.996   4.621           0.00000469 ***
## arg2                      13.793      4.070   3.389             0.000748 ***
## op_count:expensiveTRUE    51.383      5.644   9.104 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 866.7 on 588 degrees of freedom
## Multiple R-squared:  0.8708, Adjusted R-squared:  0.8697 
## F-statistic: 792.5 on 5 and 588 DF,  p-value: < 0.00000000000000022
## 
## [1] "EXP"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -83812  -9327  -3644    563 175680 
## 
## Coefficients:
##               Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    8654.71    4501.06   1.923                0.055 .  
## op_count        858.79     205.26   4.184            0.0000331 ***
## arg0            149.89     140.35   1.068                0.286    
## arg1            197.02     213.46   0.923                0.356    
## op_count:arg1   274.26      10.88  25.199 < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 31760 on 577 degrees of freedom
## Multiple R-squared:  0.8779, Adjusted R-squared:  0.8771 
## F-statistic:  1037 on 4 and 577 DF,  p-value: < 0.00000000000000022
## 
## [1] "SIGNEXTEND" "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -898.95 -302.50  -93.68  315.66 1217.16 
## 
## Coefficients:
##               Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 11389.6226    48.9540 232.660 < 0.0000000000000002 ***
## op_count       10.2144     1.3678   7.468    0.000000000000297 ***
## arg0           -0.7763     1.7860  -0.435               0.6640    
## arg1           -4.4306     1.8467  -2.399               0.0167 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 406.2 on 586 degrees of freedom
## Multiple R-squared:  0.09507,    Adjusted R-squared:  0.09044 
## F-statistic: 20.52 on 3 and 586 DF,  p-value: 0.000000000001176
## 
## [1] "LT"         "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -917.34 -337.52  -86.78  333.03 1536.89 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11331.962     56.405 200.902 <0.0000000000000002 ***
## op_count       21.287      1.486  14.324 <0.0000000000000002 ***
## arg0            2.170      2.031   1.069               0.286    
## arg1           -1.899      2.080  -0.913               0.361    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 440.2 on 582 degrees of freedom
## Multiple R-squared:  0.2622, Adjusted R-squared:  0.2584 
## F-statistic: 68.94 on 3 and 582 DF,  p-value: < 0.00000000000000022
## 
## [1] "GT"         "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -953.46 -343.38  -85.83  343.26 1323.00 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11323.0881    53.3653 212.181 <0.0000000000000002 ***
## op_count       23.2610     1.4532  16.007 <0.0000000000000002 ***
## arg0           -2.1198     2.0522  -1.033               0.302    
## arg1            0.5904     1.9762   0.299               0.765    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 433.7 on 590 degrees of freedom
## Multiple R-squared:  0.3039, Adjusted R-squared:  0.3004 
## F-statistic: 85.86 on 3 and 590 DF,  p-value: < 0.00000000000000022
## 
## [1] "SLT"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -938.19 -373.26  -55.34  377.70 1232.67 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11300.7127    60.4447 186.960 <0.0000000000000002 ***
## op_count       34.8653     1.5545  22.429 <0.0000000000000002 ***
## arg0           -2.8395     2.0922  -1.357               0.175    
## arg1            0.6169     2.0209   0.305               0.760    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 463.4 on 590 degrees of freedom
## Multiple R-squared:  0.4612, Adjusted R-squared:  0.4584 
## F-statistic: 168.3 on 3 and 590 DF,  p-value: < 0.00000000000000022
## 
## [1] "SGT"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -863.77 -328.63  -85.56  337.71 1146.15 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11289.335     52.591 214.662 <0.0000000000000002 ***
## op_count       27.728      1.419  19.535 <0.0000000000000002 ***
## arg0            2.599      2.052   1.266               0.206    
## arg1           -2.794      1.856  -1.505               0.133    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 423.1 on 588 degrees of freedom
## Multiple R-squared:  0.3961, Adjusted R-squared:  0.393 
## F-statistic: 128.6 on 3 and 588 DF,  p-value: < 0.00000000000000022
## 
## [1] "EQ"         "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1033.03  -305.55   -66.07   311.59  1129.31 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11263.0629    48.6760 231.388 <0.0000000000000002 ***
## op_count       20.8258     1.3413  15.527 <0.0000000000000002 ***
## arg0            0.3735     1.7680   0.211               0.833    
## arg1           -0.1186     1.8542  -0.064               0.949    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 399.9 on 588 degrees of freedom
## Multiple R-squared:  0.2908, Adjusted R-squared:  0.2872 
## F-statistic: 80.37 on 3 and 588 DF,  p-value: < 0.00000000000000022
## 
## [1] "ISZERO"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1165.08  -326.91   -86.64   337.00  1268.17 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11222.595     41.547 270.117 <0.0000000000000002 ***
## op_count       20.234      1.440  14.054 <0.0000000000000002 ***
## arg0            0.179      1.960   0.091               0.927    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 428.1 on 588 degrees of freedom
## Multiple R-squared:  0.2515, Adjusted R-squared:  0.2489 
## F-statistic: 98.76 on 2 and 588 DF,  p-value: < 0.00000000000000022
## 
## [1] "AND"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1048.38  -378.81   -93.21   408.77  1487.38 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11239.906     57.069 196.952 <0.0000000000000002 ***
## op_count       42.719      1.600  26.704 <0.0000000000000002 ***
## arg0            1.063      2.062   0.515               0.606    
## arg1            1.719      2.239   0.768               0.443    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 476.9 on 588 degrees of freedom
## Multiple R-squared:  0.5485, Adjusted R-squared:  0.5462 
## F-statistic: 238.1 on 3 and 588 DF,  p-value: < 0.00000000000000022
## 
## [1] "OR"         "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -987.29 -350.71  -70.27  372.71 1373.30 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11206.9722    55.5193 201.857 <0.0000000000000002 ***
## op_count       43.1459     1.5145  28.488 <0.0000000000000002 ***
## arg0            2.6844     2.0478   1.311                0.19    
## arg1            0.4383     2.0465   0.214                0.83    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 450.9 on 588 degrees of freedom
## Multiple R-squared:  0.5804, Adjusted R-squared:  0.5783 
## F-statistic: 271.2 on 3 and 588 DF,  p-value: < 0.00000000000000022
## 
## [1] "XOR"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1111.1  -374.0  -101.6   395.5  1471.8 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11413.319     58.449 195.270 <0.0000000000000002 ***
## op_count       42.726      1.621  26.358 <0.0000000000000002 ***
## arg0           -3.354      2.106  -1.593               0.112    
## arg1           -2.885      2.118  -1.362               0.174    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 482.6 on 585 degrees of freedom
## Multiple R-squared:  0.5446, Adjusted R-squared:  0.5423 
## F-statistic: 233.2 on 3 and 585 DF,  p-value: < 0.00000000000000022
## 
## [1] "NOT"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1062.2  -337.4  -106.1   362.1  1535.1 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11226.875     45.568 246.375 <0.0000000000000002 ***
## op_count       32.358      1.513  21.388 <0.0000000000000002 ***
## arg0            1.937      2.035   0.952               0.342    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 449.3 on 585 degrees of freedom
## Multiple R-squared:  0.4392, Adjusted R-squared:  0.4373 
## F-statistic: 229.1 on 2 and 585 DF,  p-value: < 0.00000000000000022
## 
## [1] "BYTE"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -874.52 -329.95  -73.92  341.87 1311.65 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11302.9390    53.9865 209.366 <0.0000000000000002 ***
## op_count       20.9830     1.4217  14.759 <0.0000000000000002 ***
## arg0           -1.7134     1.9633  -0.873               0.383    
## arg1            0.1168     1.9274   0.061               0.952    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 422.7 on 589 degrees of freedom
## Multiple R-squared:  0.2706, Adjusted R-squared:  0.2669 
## F-statistic: 72.84 on 3 and 589 DF,  p-value: < 0.00000000000000022
## 
## [1] "SHL"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1092.17  -376.27   -21.84   356.02  1513.14 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11367.693     55.963 203.130 <0.0000000000000002 ***
## op_count       27.643      1.542  17.929 <0.0000000000000002 ***
## arg0           -3.456      2.003  -1.725               0.085 .  
## arg1            1.650      2.084   0.792               0.429    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 457.9 on 586 degrees of freedom
## Multiple R-squared:  0.3566, Adjusted R-squared:  0.3533 
## F-statistic: 108.3 on 3 and 586 DF,  p-value: < 0.00000000000000022
## 
## [1] "SHR"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1003.46  -316.44   -83.65   326.01  1330.13 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11413.6468    50.0241 228.163 <0.0000000000000002 ***
## op_count       26.4995     1.4499  18.277 <0.0000000000000002 ***
## arg0           -0.9766     1.9462  -0.502               0.616    
## arg1           -3.2177     1.9185  -1.677               0.094 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 431.1 on 584 degrees of freedom
## Multiple R-squared:  0.3665, Adjusted R-squared:  0.3632 
## F-statistic: 112.6 on 3 and 584 DF,  p-value: < 0.00000000000000022
## 
## [1] "SAR"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -915.6 -340.7 -103.7  353.4 1549.0 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11276.9266    58.7186 192.050 <0.0000000000000002 ***
## op_count       37.8914     1.4898  25.434 <0.0000000000000002 ***
## arg0            0.4016     2.0530   0.196               0.845    
## arg1           -0.9820     2.0459  -0.480               0.631    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 444.1 on 587 degrees of freedom
## Multiple R-squared:  0.5244, Adjusted R-squared:  0.522 
## F-statistic: 215.8 on 3 and 587 DF,  p-value: < 0.00000000000000022
## 
## [1] "ADDRESS"    "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -775.06 -193.15    0.13  192.17  858.42 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8570.2906    18.8799   453.9 <0.0000000000000002 ***
## op_count      19.2978     0.9748    19.8 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 287.6 on 581 degrees of freedom
## Multiple R-squared:  0.4028, Adjusted R-squared:  0.4018 
## F-statistic: 391.9 on 1 and 581 DF,  p-value: < 0.00000000000000022
## 
## [1] "ORIGIN"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -812.29 -186.22   -2.34  195.80  783.44 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8586.330     19.203  447.15 <0.0000000000000002 ***
## op_count      20.108      0.992   20.27 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 293.8 on 584 degrees of freedom
## Multiple R-squared:  0.413,  Adjusted R-squared:  0.412 
## F-statistic: 410.9 on 1 and 584 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLER"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -627.77 -188.41  -20.46  187.21  787.51 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8579.1817    17.8131  481.62 <0.0000000000000002 ***
## op_count      18.5808     0.9254   20.08 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 273.8 on 584 degrees of freedom
## Multiple R-squared:  0.4084, Adjusted R-squared:  0.4074 
## F-statistic: 403.1 on 1 and 584 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLVALUE"  "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -723.45 -179.06   -7.38  159.27  734.44 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8611.1245    17.7041   486.4 <0.0000000000000002 ***
## op_count      10.1893     0.9178    11.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 269.8 on 573 degrees of freedom
## Multiple R-squared:  0.177,  Adjusted R-squared:  0.1756 
## F-statistic: 123.3 on 1 and 573 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLDATALOAD" "nethermind"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -987.0 -298.6 -102.9  220.3 1231.2 
## 
## Coefficients:
##                 Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept) 13151.912402    45.322417 290.186 <0.0000000000000002 ***
## op_count       28.200336     1.535237  18.369 <0.0000000000000002 ***
## arg0            0.001654     0.003908   0.423               0.672    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 454.8 on 582 degrees of freedom
## Multiple R-squared:  0.3672, Adjusted R-squared:  0.365 
## F-statistic: 168.9 on 2 and 582 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLDATASIZE" "nethermind"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -673.42 -186.49  -17.61  152.53  795.04 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8576.1662    16.9293  506.59 <0.0000000000000002 ***
## op_count      16.8559     0.8788   19.18 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 257.6 on 574 degrees of freedom
## Multiple R-squared:  0.3906, Adjusted R-squared:  0.3895 
## F-statistic: 367.9 on 1 and 574 DF,  p-value: < 0.00000000000000022
## 
## [1] "CALLDATACOPY" "nethermind"  
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1075.84  -365.98   -94.25   191.59  2229.93 
## 
## Coefficients:
##                    Estimate    Std. Error t value             Pr(>|t|)    
## (Intercept)   12072.8477292    90.8205385 132.931 < 0.0000000000000002 ***
## op_count        124.4101719     3.6890268  33.724 < 0.0000000000000002 ***
## arg0             -0.0007107     0.0050408  -0.141                0.888    
## arg1              0.0046291     0.0052196   0.887                0.376    
## arg2             -0.0013087     0.0075252  -0.174                0.862    
## op_count:arg2     0.0032906     0.0003892   8.456 0.000000000000000226 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 570.2 on 579 degrees of freedom
## Multiple R-squared:  0.9162, Adjusted R-squared:  0.9155 
## F-statistic:  1267 on 5 and 579 DF,  p-value: < 0.00000000000000022
## 
## [1] "CODESIZE"   "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -741.6 -176.2    0.2  173.7  764.5 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8592.5514    17.9462  478.80 <0.0000000000000002 ***
## op_count      15.4627     0.9283   16.66 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 275 on 576 degrees of freedom
## Multiple R-squared:  0.3251, Adjusted R-squared:  0.3239 
## F-statistic: 277.4 on 1 and 576 DF,  p-value: < 0.00000000000000022
## 
## [1] "CODECOPY"   "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1424.3  -556.6  -151.9   473.0  2348.1 
## 
## Coefficients:
##                    Estimate    Std. Error t value             Pr(>|t|)    
## (Intercept)   12404.2111089   125.6352091  98.732 < 0.0000000000000002 ***
## op_count        131.6384383     4.9636125  26.521 < 0.0000000000000002 ***
## arg0              0.0024251     0.0065395   0.371                0.711    
## arg1             -0.0361202     0.0064877  -5.568         0.0000000395 ***
## arg2              0.0007898     0.0103552   0.076                0.939    
## op_count:arg2     0.0029577     0.0005341   5.538         0.0000000464 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 734.3 on 581 degrees of freedom
## Multiple R-squared:  0.8739, Adjusted R-squared:  0.8729 
## F-statistic: 805.6 on 5 and 581 DF,  p-value: < 0.00000000000000022
## 
## [1] "GASPRICE"   "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -682.83 -183.65  -18.41  175.11  760.54 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8560.0681    17.6702  484.44 <0.0000000000000002 ***
## op_count      14.8534     0.9098   16.33 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 266.4 on 570 degrees of freedom
## Multiple R-squared:  0.3186, Adjusted R-squared:  0.3174 
## F-statistic: 266.5 on 1 and 570 DF,  p-value: < 0.00000000000000022
## 
## [1] "RETURNDATASIZE" "nethermind"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -766.05 -212.85   -7.36  192.54  856.87 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8579.658     19.205   446.7 <0.0000000000000002 ***
## op_count      25.946      0.994    26.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 293.3 on 576 degrees of freedom
## Multiple R-squared:  0.5419, Adjusted R-squared:  0.5411 
## F-statistic: 681.3 on 1 and 576 DF,  p-value: < 0.00000000000000022
## 
## [1] "RETURNDATACOPY" "nethermind"    
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2788.7  -650.6  -217.1   675.9  3955.4 
## 
## Coefficients:
##                    Estimate    Std. Error t value            Pr(>|t|)    
## (Intercept)   21176.2692839   143.4424530 147.629 <0.0000000000000002 ***
## op_count        141.3702343     5.9881535  23.608 <0.0000000000000002 ***
## arg0             -0.0027087     0.0087350  -0.310               0.757    
## arg1             -0.0043861     0.0080426  -0.545               0.586    
## arg2             -0.0068433     0.0123973  -0.552               0.581    
## op_count:arg2     0.0063812     0.0006387   9.991 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 924 on 585 degrees of freedom
## Multiple R-squared:  0.8746, Adjusted R-squared:  0.8735 
## F-statistic: 815.8 on 5 and 585 DF,  p-value: < 0.00000000000000022
## 
## [1] "COINBASE"   "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -700.99 -168.41   -1.01  182.58  747.01 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8576.3522    17.4307  492.02 <0.0000000000000002 ***
## op_count      20.7821     0.9017   23.05 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 265 on 579 degrees of freedom
## Multiple R-squared:  0.4785, Adjusted R-squared:  0.4776 
## F-statistic: 531.2 on 1 and 579 DF,  p-value: < 0.00000000000000022
## 
## [1] "TIMESTAMP"  "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -761.98 -188.37  -16.33  185.53  774.48 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8550.7711    17.9603  476.09 <0.0000000000000002 ***
## op_count      17.7867     0.9257   19.21 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 274.6 on 582 degrees of freedom
## Multiple R-squared:  0.3881, Adjusted R-squared:  0.3871 
## F-statistic: 369.2 on 1 and 582 DF,  p-value: < 0.00000000000000022
## 
## [1] "NUMBER"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -783.8 -207.8  -15.4  186.5  854.9 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8608.4780    19.1796  448.84 <0.0000000000000002 ***
## op_count      16.9763     0.9968   17.03 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 293.4 on 579 degrees of freedom
## Multiple R-squared:  0.3338, Adjusted R-squared:  0.3326 
## F-statistic:   290 on 1 and 579 DF,  p-value: < 0.00000000000000022
## 
## [1] "DIFFICULTY" "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -779.83 -197.81    6.73  189.19  894.23 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8608.5134    19.1464  449.62 <0.0000000000000002 ***
## op_count      18.8788     0.9953   18.97 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 292.5 on 575 degrees of freedom
## Multiple R-squared:  0.3849, Adjusted R-squared:  0.3838 
## F-statistic: 359.8 on 1 and 575 DF,  p-value: < 0.00000000000000022
## 
## [1] "GASLIMIT"   "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -924.86 -217.04   -5.95  200.73  895.88 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8577.3579    18.7152  458.31 <0.0000000000000002 ***
## op_count      17.7924     0.9708   18.33 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 286.8 on 581 degrees of freedom
## Multiple R-squared:  0.3663, Adjusted R-squared:  0.3653 
## F-statistic: 335.9 on 1 and 581 DF,  p-value: < 0.00000000000000022
## 
## [1] "CHAINID"    "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -820.03 -201.15  -14.81  180.63  923.89 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8580.2429    19.0271  450.95 <0.0000000000000002 ***
## op_count      25.4710     0.9826   25.92 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 291.1 on 583 degrees of freedom
## Multiple R-squared:  0.5355, Adjusted R-squared:  0.5347 
## F-statistic:   672 on 1 and 583 DF,  p-value: < 0.00000000000000022
## 
## [1] "SELFBALANCE" "nethermind" 
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -785.41 -202.76  -14.76  188.15  936.57 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8630.4873    19.3474  446.08 <0.0000000000000002 ***
## op_count      41.9077     0.9964   42.06 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 295.9 on 584 degrees of freedom
## Multiple R-squared:  0.7518, Adjusted R-squared:  0.7514 
## F-statistic:  1769 on 1 and 584 DF,  p-value: < 0.00000000000000022
## 
## [1] "POP"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -706.00 -272.75  -42.77  248.68 1090.41 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10101.361     33.429 302.174 < 0.0000000000000002 ***
## op_count        6.528      1.152   5.669         0.0000000225 ***
## arg0           -1.072      1.504  -0.713                0.476    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 343.3 on 591 degrees of freedom
## Multiple R-squared:  0.0523, Adjusted R-squared:  0.04909 
## F-statistic: 16.31 on 2 and 591 DF,  p-value: 0.0000001278
## 
## [1] "MLOAD"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1055.79  -344.06   -90.35   202.78  1891.65 
## 
## Coefficients:
##                 Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept) 13104.617470    52.735202 248.498 <0.0000000000000002 ***
## op_count       69.061206     1.729573  39.930 <0.0000000000000002 ***
## arg0           -0.006007     0.004472  -1.343                0.18    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 509 on 568 degrees of freedom
## Multiple R-squared:  0.7375, Adjusted R-squared:  0.7366 
## F-statistic:   798 on 2 and 568 DF,  p-value: < 0.00000000000000022
## 
## [1] "MSTORE"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -854.39 -310.79  -96.03  214.71 1280.83 
## 
## Coefficients:
##                 Estimate   Std. Error t value            Pr(>|t|)    
## (Intercept) 12157.377330    55.181926 220.314 <0.0000000000000002 ***
## op_count       57.984212     1.504737  38.534 <0.0000000000000002 ***
## arg0           -0.003292     0.003894  -0.845               0.398    
## arg1           -0.002749     0.003816  -0.720               0.472    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 445.1 on 583 degrees of freedom
## Multiple R-squared:  0.7183, Adjusted R-squared:  0.7169 
## F-statistic: 495.6 on 3 and 583 DF,  p-value: < 0.00000000000000022
## 
## [1] "MSTORE8"    "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -831.36 -244.68  -29.59  186.55  998.66 
## 
## Coefficients:
##                   Estimate     Std. Error t value            Pr(>|t|)    
## (Intercept) 12093.51369224    42.82232433 282.411 <0.0000000000000002 ***
## op_count       50.61794068     1.23148482  41.103 <0.0000000000000002 ***
## arg0           -0.00006822     0.00325809  -0.021               0.983    
## arg1            0.00323321     0.00328781   0.983               0.326    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 354.7 on 556 degrees of freedom
## Multiple R-squared:  0.7526, Adjusted R-squared:  0.7513 
## F-statistic: 563.8 on 3 and 556 DF,  p-value: < 0.00000000000000022
## 
## [1] "JUMP"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -829.53 -235.10  -37.89  231.37 1048.52 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8955.623     22.032  406.48 <0.0000000000000002 ***
## op_count      41.160      1.142   36.05 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 337.8 on 583 degrees of freedom
## Multiple R-squared:  0.6903, Adjusted R-squared:  0.6898 
## F-statistic:  1299 on 1 and 583 DF,  p-value: < 0.00000000000000022
## 
## [1] "JUMPI"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2173.1 -1010.4  -322.8   849.4  3848.6 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 12147.537    126.092  96.339 <0.0000000000000002 ***
## op_count      168.109      4.470  37.611 <0.0000000000000002 ***
## arg0           -3.159      5.762  -0.548               0.584    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1334 on 590 degrees of freedom
## Multiple R-squared:  0.7057, Adjusted R-squared:  0.7047 
## F-statistic: 707.4 on 2 and 590 DF,  p-value: < 0.00000000000000022
## 
## [1] "PC"         "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -714.94 -183.24   -2.02  187.85  856.76 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8613.8005    18.5578  464.16 <0.0000000000000002 ***
## op_count      10.2778     0.9624   10.68 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 284 on 582 degrees of freedom
## Multiple R-squared:  0.1638, Adjusted R-squared:  0.1624 
## F-statistic:   114 on 1 and 582 DF,  p-value: < 0.00000000000000022
## 
## [1] "MSIZE"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -761.8 -214.1   -3.9  188.7  767.7 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8567.2063    18.9697  451.63 <0.0000000000000002 ***
## op_count      16.7091     0.9789   17.07 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 287.7 on 578 degrees of freedom
## Multiple R-squared:  0.3351, Adjusted R-squared:  0.334 
## F-statistic: 291.3 on 1 and 578 DF,  p-value: < 0.00000000000000022
## 
## [1] "GAS"        "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -723.70 -185.10    3.86  165.24  676.72 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8598.7271    17.9738  478.40 <0.0000000000000002 ***
## op_count      13.6954     0.9322   14.69 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 273.6 on 576 degrees of freedom
## Multiple R-squared:  0.2726, Adjusted R-squared:  0.2713 
## F-statistic: 215.8 on 1 and 576 DF,  p-value: < 0.00000000000000022
## 
## [1] "JUMPDEST"   "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -888.78 -286.10  -23.69  281.02 1067.08 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 6283.423     24.917  252.18 <0.0000000000000002 ***
## op_count      41.526      1.284   32.35 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 382.2 on 582 degrees of freedom
## Multiple R-squared:  0.6426, Adjusted R-squared:  0.642 
## F-statistic:  1047 on 1 and 582 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH1"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -760.18 -189.96   -5.11  164.30  804.01 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8621.5455    18.0797  476.86 <0.0000000000000002 ***
## op_count       8.0399     0.9338    8.61 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 274.1 on 576 degrees of freedom
## Multiple R-squared:  0.114,  Adjusted R-squared:  0.1125 
## F-statistic: 74.13 on 1 and 576 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH2"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -736.57 -190.92   -2.83  179.90  786.61 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8567.0779    18.3388  467.16 <0.0000000000000002 ***
## op_count      19.3257     0.9507   20.33 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 281.3 on 584 degrees of freedom
## Multiple R-squared:  0.4143, Adjusted R-squared:  0.4133 
## F-statistic: 413.2 on 1 and 584 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH3"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -742.89 -196.12    4.72  198.38  813.12 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8594.7378    18.4833  465.00 <0.0000000000000002 ***
## op_count      18.0431     0.9489   19.02 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 280.4 on 575 degrees of freedom
## Multiple R-squared:  0.3861, Adjusted R-squared:  0.385 
## F-statistic: 361.6 on 1 and 575 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH4"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -720.22 -173.67    6.21  179.39  700.47 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8548.070     17.726   482.2 <0.0000000000000002 ***
## op_count      18.277      0.923    19.8 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 270.6 on 577 degrees of freedom
## Multiple R-squared:  0.4046, Adjusted R-squared:  0.4036 
## F-statistic: 392.1 on 1 and 577 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH5"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -744.7 -199.2   -7.7  153.5  950.2 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8583.1344    18.3076  468.83 <0.0000000000000002 ***
## op_count      18.4357     0.9409   19.59 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 279.4 on 584 degrees of freedom
## Multiple R-squared:  0.3966, Adjusted R-squared:  0.3956 
## F-statistic: 383.9 on 1 and 584 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH6"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -818.61 -219.23    1.99  176.69  741.43 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8573.3407    19.1679  447.28 <0.0000000000000002 ***
## op_count      18.2073     0.9907   18.38 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 293.1 on 581 degrees of freedom
## Multiple R-squared:  0.3676, Adjusted R-squared:  0.3665 
## F-statistic: 337.8 on 1 and 581 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH7"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -736.68 -203.71   -6.98  188.07  766.00 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8607.818     19.218  447.89 <0.0000000000000002 ***
## op_count      18.487      0.996   18.56 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 293.9 on 580 degrees of freedom
## Multiple R-squared:  0.3726, Adjusted R-squared:  0.3716 
## F-statistic: 344.5 on 1 and 580 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH8"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -702.89 -183.62  -14.21  175.35  708.86 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8586.7551    18.1484   473.1 <0.0000000000000002 ***
## op_count      18.9039     0.9405    20.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 279 on 586 degrees of freedom
## Multiple R-squared:  0.4081, Adjusted R-squared:  0.407 
## F-statistic:   404 on 1 and 586 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH9"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -789.8 -200.1   -9.3  186.3  778.1 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  8551.26      20.10  425.43 <0.0000000000000002 ***
## op_count       21.59       1.04   20.76 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 307.3 on 580 degrees of freedom
## Multiple R-squared:  0.4263, Adjusted R-squared:  0.4253 
## F-statistic: 430.9 on 1 and 580 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH10"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -860.79 -221.31  -28.98  196.67  915.77 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8601.141     20.207   425.7 <0.0000000000000002 ***
## op_count      19.106      1.044    18.3 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 309.2 on 578 degrees of freedom
## Multiple R-squared:  0.3669, Adjusted R-squared:  0.3658 
## F-statistic:   335 on 1 and 578 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH11"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -839.40 -175.72   -1.14  160.04  901.34 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8575.3982    17.9186  478.57 <0.0000000000000002 ***
## op_count      19.5224     0.9279   21.04 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 272.7 on 576 degrees of freedom
## Multiple R-squared:  0.4346, Adjusted R-squared:  0.4336 
## F-statistic: 442.7 on 1 and 576 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH12"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -804.60 -202.69   -2.28  185.75  903.27 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8604.410     19.302  445.77 <0.0000000000000002 ***
## op_count      17.586      0.998   17.62 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 296 on 585 degrees of freedom
## Multiple R-squared:  0.3468, Adjusted R-squared:  0.3456 
## F-statistic: 310.5 on 1 and 585 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH13"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -732.27 -198.83  -12.59  188.19  811.75 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8569.433     19.397   441.8 <0.0000000000000002 ***
## op_count      18.609      1.006    18.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 297.9 on 583 degrees of freedom
## Multiple R-squared:  0.3699, Adjusted R-squared:  0.3689 
## F-statistic: 342.3 on 1 and 583 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH14"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -744.16 -184.50    5.08  162.88  774.83 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8588.6404    17.6051   487.9 <0.0000000000000002 ***
## op_count      16.7873     0.9026    18.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 263.6 on 567 degrees of freedom
## Multiple R-squared:  0.3789, Adjusted R-squared:  0.3778 
## F-statistic: 345.9 on 1 and 567 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH15"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -781.92 -206.37  -15.83  193.45  873.94 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8574.182     19.412  441.69 <0.0000000000000002 ***
## op_count      16.568      1.001   16.54 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 297.4 on 583 degrees of freedom
## Multiple R-squared:  0.3195, Adjusted R-squared:  0.3183 
## F-statistic: 273.7 on 1 and 583 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH16"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -966.47 -211.53  -22.95  214.64  865.05 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8671.858     21.556  402.29 <0.0000000000000002 ***
## op_count      17.383      1.113   15.62 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 328.4 on 581 degrees of freedom
## Multiple R-squared:  0.2957, Adjusted R-squared:  0.2945 
## F-statistic: 243.9 on 1 and 581 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH17"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -914.01 -227.28  -12.44  241.57  947.46 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8729.293     21.735   401.6 <0.0000000000000002 ***
## op_count      16.998      1.126    15.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 332.2 on 578 degrees of freedom
## Multiple R-squared:  0.2829, Adjusted R-squared:  0.2817 
## F-statistic:   228 on 1 and 578 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH18"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -919.66 -226.60  -21.89  203.51 1057.30 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  8715.23      22.65   384.8 <0.0000000000000002 ***
## op_count       16.85       1.17    14.4 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 345.6 on 581 degrees of freedom
## Multiple R-squared:  0.263,  Adjusted R-squared:  0.2618 
## F-statistic: 207.4 on 1 and 581 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH19"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -787.07 -231.73  -14.31  219.96  985.68 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8712.301     21.512  405.00 <0.0000000000000002 ***
## op_count      17.702      1.112   15.92 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 327.3 on 576 degrees of freedom
## Multiple R-squared:  0.3055, Adjusted R-squared:  0.3042 
## F-statistic: 253.3 on 1 and 576 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH20"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -740.99 -206.60   -8.08  170.42  808.19 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8589.684     18.878  455.00 <0.0000000000000002 ***
## op_count      15.344      0.981   15.64 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 289.1 on 577 degrees of freedom
## Multiple R-squared:  0.2978, Adjusted R-squared:  0.2965 
## F-statistic: 244.7 on 1 and 577 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH21"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -848.89 -196.54  -11.57  200.29  898.26 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8588.2554    19.0957  449.75 <0.0000000000000002 ***
## op_count      17.5100     0.9849   17.78 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 290.6 on 578 degrees of freedom
## Multiple R-squared:  0.3535, Adjusted R-squared:  0.3524 
## F-statistic: 316.1 on 1 and 578 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH22"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -727.06 -205.82   -4.57  176.30  717.57 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8597.7711    18.3662  468.13 <0.0000000000000002 ***
## op_count      15.6734     0.9473   16.55 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 280.3 on 581 degrees of freedom
## Multiple R-squared:  0.3203, Adjusted R-squared:  0.3191 
## F-statistic: 273.8 on 1 and 581 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH23"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -824.86 -199.69  -17.85  196.23  818.42 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8571.614     19.723  434.61 <0.0000000000000002 ***
## op_count      17.500      1.015   17.25 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 300.6 on 584 degrees of freedom
## Multiple R-squared:  0.3375, Adjusted R-squared:  0.3363 
## F-statistic: 297.5 on 1 and 584 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH24"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -807.09 -182.55  -17.22  178.50  796.67 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8564.7155    18.0039  475.71 <0.0000000000000002 ***
## op_count      18.9060     0.9302   20.32 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 275.2 on 580 degrees of freedom
## Multiple R-squared:  0.416,  Adjusted R-squared:  0.415 
## F-statistic: 413.1 on 1 and 580 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH25"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -685.9 -190.3   -4.4  179.8  759.5 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8615.6263    18.4838  466.12 <0.0000000000000002 ***
## op_count      15.1888     0.9535   15.93 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 283.2 on 583 degrees of freedom
## Multiple R-squared:  0.3032, Adjusted R-squared:  0.3021 
## F-statistic: 253.7 on 1 and 583 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH26"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -735.61 -228.23  -25.25  210.97  951.19 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8602.794     20.790  413.79 <0.0000000000000002 ***
## op_count      16.416      1.074   15.28 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 318.2 on 585 degrees of freedom
## Multiple R-squared:  0.2853, Adjusted R-squared:  0.2841 
## F-statistic: 233.5 on 1 and 585 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH27"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -738.13 -222.93  -28.73  219.68  831.22 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8571.381     19.743  434.16 <0.0000000000000002 ***
## op_count      16.496      1.019   16.19 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 302.1 on 585 degrees of freedom
## Multiple R-squared:  0.3095, Adjusted R-squared:  0.3083 
## F-statistic: 262.2 on 1 and 585 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH28"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -786.15 -224.20  -14.59  196.75  877.41 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8596.844     20.337  422.72 <0.0000000000000002 ***
## op_count      14.241      1.048   13.59 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 309.2 on 581 degrees of freedom
## Multiple R-squared:  0.2412, Adjusted R-squared:  0.2399 
## F-statistic: 184.7 on 1 and 581 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH29"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -821.77 -197.36   -8.19  203.43  893.98 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8622.589     19.602  439.89 <0.0000000000000002 ***
## op_count      13.732      1.019   13.48 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 300.6 on 582 degrees of freedom
## Multiple R-squared:  0.2379, Adjusted R-squared:  0.2366 
## F-statistic: 181.7 on 1 and 582 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH30"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -696.08 -195.04  -13.31  192.26  932.64 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8621.4474    18.9917  453.96 <0.0000000000000002 ***
## op_count      13.0241     0.9787   13.31 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 290.7 on 586 degrees of freedom
## Multiple R-squared:  0.2321, Adjusted R-squared:  0.2307 
## F-statistic: 177.1 on 1 and 586 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH31"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -780.85 -211.14   -0.23  195.06  934.35 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8618.609     19.554  440.76 <0.0000000000000002 ***
## op_count      14.705      1.005   14.63 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 297.2 on 582 degrees of freedom
## Multiple R-squared:  0.269,  Adjusted R-squared:  0.2677 
## F-statistic: 214.2 on 1 and 582 DF,  p-value: < 0.00000000000000022
## 
## [1] "PUSH32"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -834.58 -196.37  -11.14  185.44  849.52 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 8607.6226    19.1220  450.14 <0.0000000000000002 ***
## op_count      11.2099     0.9878   11.35 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 292.6 on 584 degrees of freedom
## Multiple R-squared:  0.1807, Adjusted R-squared:  0.1793 
## F-statistic: 128.8 on 1 and 584 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP1"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -904.55 -291.17  -78.16  287.97 1243.54 
## 
## Coefficients:
##               Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 11269.2199    39.0196 288.809 < 0.0000000000000002 ***
## op_count       10.5183     1.3124   8.015  0.00000000000000604 ***
## arg0           -0.7194     1.7478  -0.412                0.681    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 389.8 on 584 degrees of freedom
## Multiple R-squared:  0.09927,    Adjusted R-squared:  0.09619 
## F-statistic: 32.18 on 2 and 584 DF,  p-value: 0.00000000000005509
## 
## [1] "DUP2"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -967.73 -310.13  -52.55  314.06 1337.91 
## 
## Coefficients:
##               Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 11262.8770    38.0726 295.827 < 0.0000000000000002 ***
## op_count       10.8506     1.3480   8.050  0.00000000000000456 ***
## arg0            0.8271     1.7135   0.483                0.629    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 403.4 on 593 degrees of freedom
## Multiple R-squared:  0.09886,    Adjusted R-squared:  0.09582 
## F-statistic: 32.53 on 2 and 593 DF,  p-value: 0.00000000000003939
## 
## [1] "DUP3"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1130.10  -323.99   -50.49   334.12  1217.48 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 11338.644     39.450 287.422 < 0.0000000000000002 ***
## op_count        9.482      1.340   7.074     0.00000000000431 ***
## arg0           -3.419      1.846  -1.852               0.0645 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 398.5 on 587 degrees of freedom
## Multiple R-squared:  0.0841, Adjusted R-squared:  0.08098 
## F-statistic: 26.95 on 2 and 587 DF,  p-value: 0.000000000006343
## 
## [1] "DUP4"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -973.28 -295.11  -34.31  309.26 1263.99 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11228.066     37.774 297.247 <0.0000000000000002 ***
## op_count       11.064      1.302   8.497 <0.0000000000000002 ***
## arg0            1.576      1.704   0.925               0.355    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 388.7 on 589 degrees of freedom
## Multiple R-squared:  0.1103, Adjusted R-squared:  0.1072 
## F-statistic:  36.5 on 2 and 589 DF,  p-value: 0.00000000000000114
## 
## [1] "DUP5"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -990.50 -311.58  -58.51  325.82 1368.32 
## 
## Coefficients:
##               Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 11288.0627    39.0529 289.046 < 0.0000000000000002 ***
## op_count       10.4295     1.3663   7.633   0.0000000000000936 ***
## arg0           -0.2588     1.7371  -0.149                0.882    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 405.3 on 585 degrees of freedom
## Multiple R-squared:  0.0906, Adjusted R-squared:  0.08749 
## F-statistic: 29.14 on 2 and 585 DF,  p-value: 0.000000000000863
## 
## [1] "DUP6"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -940.58 -367.30  -40.53  334.94 1444.33 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 11202.520     44.875 249.637 < 0.0000000000000002 ***
## op_count       11.153      1.467   7.605    0.000000000000113 ***
## arg0            3.861      2.027   1.904               0.0573 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 438.3 on 592 degrees of freedom
## Multiple R-squared:  0.09393,    Adjusted R-squared:  0.09087 
## F-statistic: 30.69 on 2 and 592 DF,  p-value: 0.0000000000002089
## 
## [1] "DUP7"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1180.40  -327.36   -33.56   308.45  1321.45 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 11327.659     42.647 265.612 < 0.0000000000000002 ***
## op_count       10.325      1.435   7.193     0.00000000000195 ***
## arg0            1.543      1.970   0.783                0.434    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 426.3 on 585 degrees of freedom
## Multiple R-squared:  0.0821, Adjusted R-squared:  0.07897 
## F-statistic: 26.16 on 2 and 585 DF,  p-value: 0.0000000000131
## 
## [1] "DUP8"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -958.79 -317.58  -46.39  338.34 1341.96 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11259.113     39.381 285.905 <0.0000000000000002 ***
## op_count       11.884      1.382   8.598 <0.0000000000000002 ***
## arg0            4.169      1.902   2.192              0.0288 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 409.4 on 583 degrees of freedom
## Multiple R-squared:  0.1191, Adjusted R-squared:  0.1161 
## F-statistic:  39.4 on 2 and 583 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP9"       "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -966.57 -350.47  -25.69  332.88 1237.90 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11283.186     42.637 264.632 <0.0000000000000002 ***
## op_count       12.315      1.447   8.510 <0.0000000000000002 ***
## arg0            1.042      1.987   0.524                 0.6    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 428.1 on 584 degrees of freedom
## Multiple R-squared:  0.1107, Adjusted R-squared:  0.1076 
## F-statistic: 36.34 on 2 and 584 DF,  p-value: 0.000000000000001333
## 
## [1] "DUP10"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -935.05 -306.12  -32.03  328.39 1077.77 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11259.7377    40.4552 278.326 <0.0000000000000002 ***
## op_count       12.2482     1.3563   9.030 <0.0000000000000002 ***
## arg0            0.7413     1.7385   0.426                0.67    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 402.3 on 583 degrees of freedom
## Multiple R-squared:  0.1229, Adjusted R-squared:  0.1199 
## F-statistic: 40.86 on 2 and 583 DF,  p-value: < 0.00000000000000022
## 
## [1] "DUP11"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -913.36 -357.20  -77.92  346.75 1547.33 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 11417.218     45.434 251.291 < 0.0000000000000002 ***
## op_count       10.841      1.518   7.142     0.00000000000273 ***
## arg0           -1.772      2.047  -0.866                0.387    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 450.8 on 588 degrees of freedom
## Multiple R-squared:  0.08104,    Adjusted R-squared:  0.07792 
## F-statistic: 25.93 on 2 and 588 DF,  p-value: 0.00000000001617
## 
## [1] "DUP12"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -969.03 -312.80  -81.79  307.36 1208.82 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 11289.313     39.365 286.783 < 0.0000000000000002 ***
## op_count        9.755      1.371   7.114      0.0000000000033 ***
## arg0           -1.516      1.780  -0.852                0.395    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 408.2 on 585 degrees of freedom
## Multiple R-squared:  0.08061,    Adjusted R-squared:  0.07747 
## F-statistic: 25.65 on 2 and 585 DF,  p-value: 0.00000000002106
## 
## [1] "DUP13"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -885.14 -320.69  -64.74  314.82 1229.80 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 11282.997     43.029 262.215 <0.0000000000000002 ***
## op_count       12.079      1.422   8.496 <0.0000000000000002 ***
## arg0            0.699      1.923   0.364               0.716    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 423.8 on 588 degrees of freedom
## Multiple R-squared:  0.1095, Adjusted R-squared:  0.1064 
## F-statistic: 36.14 on 2 and 588 DF,  p-value: 0.000000000000001576
## 
## [1] "DUP14"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -956.68 -295.60  -76.71  285.42 1135.30 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 11241.972     38.441 292.450 < 0.0000000000000002 ***
## op_count       10.878      1.319   8.249  0.00000000000000105 ***
## arg0            2.403      1.774   1.354                0.176    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 393.6 on 587 degrees of freedom
## Multiple R-squared:  0.1063, Adjusted R-squared:  0.1033 
## F-statistic: 34.92 on 2 and 587 DF,  p-value: 0.0000000000000047
## 
## [1] "DUP15"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -979.79 -327.67  -82.56  308.39 1482.68 
## 
## Coefficients:
##              Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 11229.721     43.758 256.633 < 0.0000000000000002 ***
## op_count        9.215      1.391   6.622      0.0000000000796 ***
## arg0            1.647      1.990   0.828                0.408    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 415.9 on 592 degrees of freedom
## Multiple R-squared:  0.07001,    Adjusted R-squared:  0.06687 
## F-statistic: 22.28 on 2 and 592 DF,  p-value: 0.000000000467
## 
## [1] "DUP16"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -947.29 -343.57  -87.61  322.18 1419.25 
## 
## Coefficients:
##               Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 11429.3029    42.3430 269.922 < 0.0000000000000002 ***
## op_count       11.8094     1.4341   8.235  0.00000000000000118 ***
## arg0            0.7962     1.9049   0.418                0.676    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 425.4 on 584 degrees of freedom
## Multiple R-squared:  0.1042, Adjusted R-squared:  0.1012 
## F-statistic: 33.98 on 2 and 584 DF,  p-value: 0.00000000000001098
## 
## [1] "SWAP1"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -754.60 -267.08  -19.53  249.10  994.76 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10119.1725    42.2892 239.285 <0.0000000000000002 ***
## op_count       11.9207     1.1579  10.295 <0.0000000000000002 ***
## arg0            0.8334     1.5545   0.536               0.592    
## arg1           -0.4021     1.5130  -0.266               0.791    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 345.2 on 587 degrees of freedom
## Multiple R-squared:  0.1534, Adjusted R-squared:  0.1491 
## F-statistic: 35.46 on 3 and 587 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP2"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -985.9 -268.2  -46.4  266.0 1180.5 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10126.5937    44.7158 226.466 <0.0000000000000002 ***
## op_count       12.8478     1.2008  10.699 <0.0000000000000002 ***
## arg0           -1.7233     1.6628  -1.036               0.300    
## arg1           -0.2961     1.5918  -0.186               0.853    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 357.5 on 584 degrees of freedom
## Multiple R-squared:  0.1652, Adjusted R-squared:  0.1609 
## F-statistic: 38.52 on 3 and 584 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP3"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -897.63 -246.43  -29.34  244.33  982.20 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10178.039     40.857 249.111 <0.0000000000000002 ***
## op_count       12.739      1.139  11.186 <0.0000000000000002 ***
## arg0           -1.576      1.506  -1.046               0.296    
## arg1           -1.912      1.461  -1.308               0.191    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 336.9 on 583 degrees of freedom
## Multiple R-squared:  0.1792, Adjusted R-squared:  0.1749 
## F-statistic: 42.41 on 3 and 583 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP4"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -935.11 -252.39  -49.44  243.34  998.56 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10057.229     42.445 236.947 <0.0000000000000002 ***
## op_count       12.749      1.140  11.182 <0.0000000000000002 ***
## arg0           -1.559      1.584  -0.984              0.3253    
## arg1            3.030      1.512   2.004              0.0455 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 339 on 582 degrees of freedom
## Multiple R-squared:  0.1822, Adjusted R-squared:  0.178 
## F-statistic: 43.22 on 3 and 582 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP5"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1003.84  -255.25   -46.51   260.39   996.12 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10126.2660    45.6735 221.710 <0.0000000000000002 ***
## op_count       11.9691     1.1787  10.154 <0.0000000000000002 ***
## arg0            0.3563     1.5632   0.228               0.820    
## arg1           -1.5470     1.6845  -0.918               0.359    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 350 on 585 degrees of freedom
## Multiple R-squared:  0.1512, Adjusted R-squared:  0.1469 
## F-statistic: 34.74 on 3 and 585 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP6"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -869.02 -246.08  -44.88  235.45 1030.51 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10064.014     43.786 229.844 <0.0000000000000002 ***
## op_count       12.409      1.144  10.846 <0.0000000000000002 ***
## arg0            2.000      1.531   1.306               0.192    
## arg1            2.276      1.519   1.499               0.134    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 340.6 on 589 degrees of freedom
## Multiple R-squared:  0.1708, Adjusted R-squared:  0.1666 
## F-statistic: 40.45 on 3 and 589 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP7"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -824.77 -252.35  -40.45  231.21 1012.95 
## 
## Coefficients:
##               Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) 10104.3488    44.2203 228.500 < 0.0000000000000002 ***
## op_count       12.9220     1.1708  11.037 < 0.0000000000000002 ***
## arg0           -0.7647     1.6217  -0.472              0.63742    
## arg1            4.8788     1.6667   2.927              0.00355 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 346.8 on 584 degrees of freedom
## Multiple R-squared:  0.1818, Adjusted R-squared:  0.1776 
## F-statistic: 43.26 on 3 and 584 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP8"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -810.19 -267.25  -22.93  270.70 1085.00 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10068.2050    41.7488 241.161 <0.0000000000000002 ***
## op_count       14.6038     1.1752  12.427 <0.0000000000000002 ***
## arg0            0.8341     1.5727   0.530               0.596    
## arg1            0.5642     1.6035   0.352               0.725    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 350.3 on 585 degrees of freedom
## Multiple R-squared:  0.2093, Adjusted R-squared:  0.2052 
## F-statistic: 51.62 on 3 and 585 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP9"      "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1027.25  -255.86   -27.35   288.04  1092.56 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10117.7156    47.7695 211.803 <0.0000000000000002 ***
## op_count       13.6676     1.2168  11.232 <0.0000000000000002 ***
## arg0           -0.3033     1.7212  -0.176               0.860    
## arg1           -0.7178     1.6270  -0.441               0.659    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 362.7 on 589 degrees of freedom
## Multiple R-squared:  0.1767, Adjusted R-squared:  0.1725 
## F-statistic: 42.14 on 3 and 589 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP10"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -801.78 -283.50  -48.87  282.96 1095.58 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10222.1139    46.2483 221.027 <0.0000000000000002 ***
## op_count       14.6883     1.1981  12.260 <0.0000000000000002 ***
## arg0           -0.2537     1.5544  -0.163               0.870    
## arg1           -0.6171     1.6901  -0.365               0.715    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 357.2 on 587 degrees of freedom
## Multiple R-squared:  0.204,  Adjusted R-squared:    0.2 
## F-statistic: 50.15 on 3 and 587 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP11"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -692.62 -276.48  -55.97  266.26 1102.46 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10052.9470    43.1044 233.223 <0.0000000000000002 ***
## op_count       13.7845     1.2031  11.458 <0.0000000000000002 ***
## arg0            1.6605     1.6557   1.003               0.316    
## arg1            0.3939     1.6259   0.242               0.809    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 358.2 on 587 degrees of freedom
## Multiple R-squared:  0.1842, Adjusted R-squared:   0.18 
## F-statistic: 44.17 on 3 and 587 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP12"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -931.40 -278.83  -52.16  268.33 1104.50 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10140.769     42.946 236.126 <0.0000000000000002 ***
## op_count       14.361      1.232  11.661 <0.0000000000000002 ***
## arg0            1.620      1.590   1.019               0.309    
## arg1           -1.879      1.632  -1.152               0.250    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 365.7 on 585 degrees of freedom
## Multiple R-squared:  0.1905, Adjusted R-squared:  0.1864 
## F-statistic:  45.9 on 3 and 585 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP13"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -756.23 -264.31  -26.06  255.00 1068.64 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10156.2557    41.0362 247.495 <0.0000000000000002 ***
## op_count       11.7226     1.1751   9.976 <0.0000000000000002 ***
## arg0           -0.7732     1.5555  -0.497               0.619    
## arg1            1.3540     1.5397   0.879               0.380    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 347.6 on 583 degrees of freedom
## Multiple R-squared:  0.1469, Adjusted R-squared:  0.1425 
## F-statistic: 33.46 on 3 and 583 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP14"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1046.71  -285.80   -37.67   268.08  1192.07 
## 
## Coefficients:
##                Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept) 10088.79686    44.27695 227.857 <0.0000000000000002 ***
## op_count       12.31666     1.23850   9.945 <0.0000000000000002 ***
## arg0            1.94719     1.73644   1.121               0.263    
## arg1           -0.03987     1.64756  -0.024               0.981    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 370.1 on 589 degrees of freedom
## Multiple R-squared:  0.1454, Adjusted R-squared:  0.141 
## F-statistic: 33.39 on 3 and 589 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP15"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -748.78 -283.34  -51.76  266.59 1119.72 
## 
## Coefficients:
##               Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 10343.6548    46.0831 224.457 <0.0000000000000002 ***
## op_count       11.7495     1.2272   9.574 <0.0000000000000002 ***
## arg0           -0.1859     1.6403  -0.113              0.9098    
## arg1           -2.7067     1.6395  -1.651              0.0993 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 366.3 on 586 degrees of freedom
## Multiple R-squared:  0.1387, Adjusted R-squared:  0.1342 
## F-statistic: 31.44 on 3 and 586 DF,  p-value: < 0.00000000000000022
## 
## [1] "SWAP16"     "nethermind"
## 
## Call:
## lm(formula = formula, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -909.03 -276.99  -36.49  263.15 1062.03 
## 
## Coefficients:
##                Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept) 10193.81279    45.22262 225.414 <0.0000000000000002 ***
## op_count       13.01123     1.23277  10.554 <0.0000000000000002 ***
## arg0            0.07923     1.57255   0.050               0.960    
## arg1            1.59828     1.69744   0.942               0.347    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 367.5 on 590 degrees of freedom
## Multiple R-squared:  0.1598, Adjusted R-squared:  0.1556 
## F-statistic: 37.41 on 3 and 590 DF,  p-value: < 0.00000000000000022
estimates
##             opcode        env has_significant has_impacting
## 1              ADD nethermind           FALSE         FALSE
## 2              MUL nethermind           FALSE         FALSE
## 3              SUB nethermind           FALSE         FALSE
## 4              DIV nethermind            TRUE          TRUE
## 5             SDIV nethermind            TRUE          TRUE
## 6              MOD nethermind            TRUE          TRUE
## 7             SMOD nethermind            TRUE          TRUE
## 8           ADDMOD nethermind            TRUE          TRUE
## 9           MULMOD nethermind            TRUE          TRUE
## 10             EXP nethermind            TRUE          TRUE
## 11      SIGNEXTEND nethermind           FALSE         FALSE
## 12              LT nethermind           FALSE         FALSE
## 13              GT nethermind           FALSE         FALSE
## 14             SLT nethermind           FALSE         FALSE
## 15             SGT nethermind           FALSE         FALSE
## 16              EQ nethermind           FALSE         FALSE
## 17          ISZERO nethermind           FALSE         FALSE
## 18             AND nethermind           FALSE         FALSE
## 19              OR nethermind           FALSE         FALSE
## 20             XOR nethermind           FALSE         FALSE
## 21             NOT nethermind           FALSE         FALSE
## 22            BYTE nethermind           FALSE         FALSE
## 23             SHL nethermind           FALSE         FALSE
## 24             SHR nethermind           FALSE         FALSE
## 25             SAR nethermind           FALSE         FALSE
## 26         ADDRESS nethermind           FALSE         FALSE
## 27          ORIGIN nethermind           FALSE         FALSE
## 28          CALLER nethermind           FALSE         FALSE
## 29       CALLVALUE nethermind           FALSE         FALSE
## 30    CALLDATALOAD nethermind           FALSE         FALSE
## 31    CALLDATASIZE nethermind           FALSE         FALSE
## 32    CALLDATACOPY nethermind            TRUE          TRUE
## 33        CODESIZE nethermind           FALSE         FALSE
## 34        CODECOPY nethermind            TRUE          TRUE
## 35        GASPRICE nethermind           FALSE         FALSE
## 36  RETURNDATASIZE nethermind           FALSE         FALSE
## 37  RETURNDATACOPY nethermind            TRUE          TRUE
## 38        COINBASE nethermind           FALSE         FALSE
## 39       TIMESTAMP nethermind           FALSE         FALSE
## 40          NUMBER nethermind           FALSE         FALSE
## 41      DIFFICULTY nethermind           FALSE         FALSE
## 42        GASLIMIT nethermind           FALSE         FALSE
## 43         CHAINID nethermind           FALSE         FALSE
## 44     SELFBALANCE nethermind           FALSE         FALSE
## 45             POP nethermind           FALSE         FALSE
## 46           MLOAD nethermind           FALSE         FALSE
## 47          MSTORE nethermind           FALSE         FALSE
## 48         MSTORE8 nethermind           FALSE         FALSE
## 49            JUMP nethermind           FALSE         FALSE
## 50           JUMPI nethermind           FALSE         FALSE
## 51              PC nethermind           FALSE         FALSE
## 52           MSIZE nethermind           FALSE         FALSE
## 53             GAS nethermind           FALSE         FALSE
## 54        JUMPDEST nethermind           FALSE         FALSE
## 55           PUSH1 nethermind           FALSE         FALSE
## 56           PUSH2 nethermind           FALSE         FALSE
## 57           PUSH3 nethermind           FALSE         FALSE
## 58           PUSH4 nethermind           FALSE         FALSE
## 59           PUSH5 nethermind           FALSE         FALSE
## 60           PUSH6 nethermind           FALSE         FALSE
## 61           PUSH7 nethermind           FALSE         FALSE
## 62           PUSH8 nethermind           FALSE         FALSE
## 63           PUSH9 nethermind           FALSE         FALSE
## 64          PUSH10 nethermind           FALSE         FALSE
## 65          PUSH11 nethermind           FALSE         FALSE
## 66          PUSH12 nethermind           FALSE         FALSE
## 67          PUSH13 nethermind           FALSE         FALSE
## 68          PUSH14 nethermind           FALSE         FALSE
## 69          PUSH15 nethermind           FALSE         FALSE
## 70          PUSH16 nethermind           FALSE         FALSE
## 71          PUSH17 nethermind           FALSE         FALSE
## 72          PUSH18 nethermind           FALSE         FALSE
## 73          PUSH19 nethermind           FALSE         FALSE
## 74          PUSH20 nethermind           FALSE         FALSE
## 75          PUSH21 nethermind           FALSE         FALSE
## 76          PUSH22 nethermind           FALSE         FALSE
## 77          PUSH23 nethermind           FALSE         FALSE
## 78          PUSH24 nethermind           FALSE         FALSE
## 79          PUSH25 nethermind           FALSE         FALSE
## 80          PUSH26 nethermind           FALSE         FALSE
## 81          PUSH27 nethermind           FALSE         FALSE
## 82          PUSH28 nethermind           FALSE         FALSE
## 83          PUSH29 nethermind           FALSE         FALSE
## 84          PUSH30 nethermind           FALSE         FALSE
## 85          PUSH31 nethermind           FALSE         FALSE
## 86          PUSH32 nethermind           FALSE         FALSE
## 87            DUP1 nethermind           FALSE         FALSE
## 88            DUP2 nethermind           FALSE         FALSE
## 89            DUP3 nethermind           FALSE         FALSE
## 90            DUP4 nethermind           FALSE         FALSE
## 91            DUP5 nethermind           FALSE         FALSE
## 92            DUP6 nethermind           FALSE         FALSE
## 93            DUP7 nethermind           FALSE         FALSE
## 94            DUP8 nethermind           FALSE         FALSE
## 95            DUP9 nethermind           FALSE         FALSE
## 96           DUP10 nethermind           FALSE         FALSE
## 97           DUP11 nethermind           FALSE         FALSE
## 98           DUP12 nethermind           FALSE         FALSE
## 99           DUP13 nethermind           FALSE         FALSE
## 100          DUP14 nethermind           FALSE         FALSE
## 101          DUP15 nethermind           FALSE         FALSE
## 102          DUP16 nethermind           FALSE         FALSE
## 103          SWAP1 nethermind           FALSE         FALSE
## 104          SWAP2 nethermind           FALSE         FALSE
## 105          SWAP3 nethermind           FALSE         FALSE
## 106          SWAP4 nethermind           FALSE         FALSE
## 107          SWAP5 nethermind           FALSE         FALSE
## 108          SWAP6 nethermind           FALSE         FALSE
## 109          SWAP7 nethermind           FALSE         FALSE
## 110          SWAP8 nethermind           FALSE         FALSE
## 111          SWAP9 nethermind           FALSE         FALSE
## 112         SWAP10 nethermind           FALSE         FALSE
## 113         SWAP11 nethermind           FALSE         FALSE
## 114         SWAP12 nethermind           FALSE         FALSE
## 115         SWAP13 nethermind           FALSE         FALSE
## 116         SWAP14 nethermind           FALSE         FALSE
## 117         SWAP15 nethermind           FALSE         FALSE
## 118         SWAP16 nethermind           FALSE         FALSE
##     estimate_marginal_ns arg0_ns          arg1_ns             arg2_ns
## 1       31.4480362646858    <NA>             <NA>                <NA>
## 2       61.7786313605039    <NA>             <NA>                <NA>
## 3       31.5026898922074    <NA>             <NA>                <NA>
## 4       29.7124999719411    <NA>             <NA>                <NA>
## 5       44.3956105761923    <NA>             <NA>                <NA>
## 6       29.1611483326222    <NA>             <NA>                <NA>
## 7       52.3264911399163    <NA>             <NA>                <NA>
## 8       54.7506374499178    <NA>             <NA>                <NA>
## 9       137.088751995398    <NA>             <NA>                <NA>
## 10      858.792164462129    <NA> 274.257023617893                <NA>
## 11      10.2144122709573    <NA>             <NA>                <NA>
## 12      21.2865489397508    <NA>             <NA>                <NA>
## 13      23.2609624999515    <NA>             <NA>                <NA>
## 14      34.8653453181971    <NA>             <NA>                <NA>
## 15      27.7275021004007    <NA>             <NA>                <NA>
## 16        20.82583406751    <NA>             <NA>                <NA>
## 17      20.2340034179102    <NA>             <NA>                <NA>
## 18      42.7192381656865    <NA>             <NA>                <NA>
## 19      43.1458898244736    <NA>             <NA>                <NA>
## 20      42.7257830685645    <NA>             <NA>                <NA>
## 21      32.3576977650141    <NA>             <NA>                <NA>
## 22      20.9830023696138    <NA>             <NA>                <NA>
## 23      27.6430586234422    <NA>             <NA>                <NA>
## 24      26.4994703718385    <NA>             <NA>                <NA>
## 25      37.8913563341062    <NA>             <NA>                <NA>
## 26      19.2977539314556    <NA>             <NA>                <NA>
## 27      20.1084398101392    <NA>             <NA>                <NA>
## 28      18.5808263990613    <NA>             <NA>                <NA>
## 29      10.1892655582266    <NA>             <NA>                <NA>
## 30      28.2003357691992    <NA>             <NA>                <NA>
## 31      16.8559203684234    <NA>             <NA>                <NA>
## 32      124.410171931004    <NA>             <NA> 0.00329064668859508
## 33       15.462669442162    <NA>             <NA>                <NA>
## 34      131.638438321262    <NA>             <NA> 0.00295773948264731
## 35      14.8534316857784    <NA>             <NA>                <NA>
## 36      25.9456016353638    <NA>             <NA>                <NA>
## 37      141.370234297858    <NA>             <NA> 0.00638116257436516
## 38      20.7821182486622    <NA>             <NA>                <NA>
## 39      17.7867015233196    <NA>             <NA>                <NA>
## 40      16.9762809632619    <NA>             <NA>                <NA>
## 41      18.8788364744263    <NA>             <NA>                <NA>
## 42       17.792435360199    <NA>             <NA>                <NA>
## 43      25.4710316247015    <NA>             <NA>                <NA>
## 44       41.907693396385    <NA>             <NA>                <NA>
## 45      6.52765575600859    <NA>             <NA>                <NA>
## 46      69.0612062213677    <NA>             <NA>                <NA>
## 47      57.9842117375483    <NA>             <NA>                <NA>
## 48      50.6179406815128    <NA>             <NA>                <NA>
## 49      41.1601814463512    <NA>             <NA>                <NA>
## 50      168.109447769624    <NA>             <NA>                <NA>
## 51      10.2778063750932    <NA>             <NA>                <NA>
## 52      16.7090746617699    <NA>             <NA>                <NA>
## 53      13.6953560581785    <NA>             <NA>                <NA>
## 54      41.5263917618967    <NA>             <NA>                <NA>
## 55      8.03992755484684    <NA>             <NA>                <NA>
## 56      19.3256695159143    <NA>             <NA>                <NA>
## 57      18.0430724888324    <NA>             <NA>                <NA>
## 58      18.2772378239647    <NA>             <NA>                <NA>
## 59      18.4356589478897    <NA>             <NA>                <NA>
## 60      18.2072778597174    <NA>             <NA>                <NA>
## 61       18.487075091643    <NA>             <NA>                <NA>
## 62      18.9039108858216    <NA>             <NA>                <NA>
## 63      21.5911280981112    <NA>             <NA>                <NA>
## 64      19.1062505880463    <NA>             <NA>                <NA>
## 65      19.5224052605334    <NA>             <NA>                <NA>
## 66      17.5859076541569    <NA>             <NA>                <NA>
## 67      18.6086768661342    <NA>             <NA>                <NA>
## 68      16.7873407719655    <NA>             <NA>                <NA>
## 69      16.5675658603927    <NA>             <NA>                <NA>
## 70      17.3829361893053    <NA>             <NA>                <NA>
## 71      16.9980110756702    <NA>             <NA>                <NA>
## 72      16.8461135926543    <NA>             <NA>                <NA>
## 73      17.7021791171376    <NA>             <NA>                <NA>
## 74      15.3443757018529    <NA>             <NA>                <NA>
## 75      17.5100369816357    <NA>             <NA>                <NA>
## 76      15.6733544121339    <NA>             <NA>                <NA>
## 77      17.5003529342986    <NA>             <NA>                <NA>
## 78      18.9059740244977    <NA>             <NA>                <NA>
## 79      15.1888076813989    <NA>             <NA>                <NA>
## 80      16.4163693785316    <NA>             <NA>                <NA>
## 81      16.4956947849421    <NA>             <NA>                <NA>
## 82      14.2407749334242    <NA>             <NA>                <NA>
## 83      13.7316783440816    <NA>             <NA>                <NA>
## 84      13.0240528199439    <NA>             <NA>                <NA>
## 85      14.7046741927553    <NA>             <NA>                <NA>
## 86      11.2098918666509    <NA>             <NA>                <NA>
## 87      10.5183258337575    <NA>             <NA>                <NA>
## 88      10.8506016826713    <NA>             <NA>                <NA>
## 89      9.48212389043194    <NA>             <NA>                <NA>
## 90      11.0640348694298    <NA>             <NA>                <NA>
## 91      10.4294710456491    <NA>             <NA>                <NA>
## 92      11.1534180300148    <NA>             <NA>                <NA>
## 93      10.3249134216255    <NA>             <NA>                <NA>
## 94      11.8838546341636    <NA>             <NA>                <NA>
## 95      12.3145998430019    <NA>             <NA>                <NA>
## 96      12.2481878785853    <NA>             <NA>                <NA>
## 97      10.8406425368947    <NA>             <NA>                <NA>
## 98      9.75458526094098    <NA>             <NA>                <NA>
## 99      12.0789679330047    <NA>             <NA>                <NA>
## 100     10.8776744242078    <NA>             <NA>                <NA>
## 101     9.21454867853761    <NA>             <NA>                <NA>
## 102     11.8093848915208    <NA>             <NA>                <NA>
## 103     11.9206710287547    <NA>             <NA>                <NA>
## 104     12.8478090965134    <NA>             <NA>                <NA>
## 105      12.739001083069    <NA>             <NA>                <NA>
## 106     12.7492154624927    <NA>             <NA>                <NA>
## 107     11.9690990535591    <NA>             <NA>                <NA>
## 108     12.4086487099253    <NA>             <NA>                <NA>
## 109     12.9219526656711    <NA>             <NA>                <NA>
## 110     14.6037676623349    <NA>             <NA>                <NA>
## 111     13.6675926650132    <NA>             <NA>                <NA>
## 112     14.6883246175182    <NA>             <NA>                <NA>
## 113     13.7845430656563    <NA>             <NA>                <NA>
## 114     14.3613399049957    <NA>             <NA>                <NA>
## 115     11.7225517287966    <NA>             <NA>                <NA>
## 116     12.3166597985577    <NA>             <NA>                <NA>
## 117     11.7495172007354    <NA>             <NA>                <NA>
## 118      13.011227097562    <NA>             <NA>                <NA>
##         expensive_ns arg0_ns_stderr   arg1_ns_stderr       arg2_ns_stderr
## 1               <NA>           <NA>             <NA>                 <NA>
## 2               <NA>           <NA>             <NA>                 <NA>
## 3               <NA>           <NA>             <NA>                 <NA>
## 4   35.4153981950679           <NA>             <NA>                 <NA>
## 5   43.1527202060663           <NA>             <NA>                 <NA>
## 6   36.3036859678608           <NA>             <NA>                 <NA>
## 7    39.267949590233           <NA>             <NA>                 <NA>
## 8    38.586740587816           <NA>             <NA>                 <NA>
## 9   51.3827768858027           <NA>             <NA>                 <NA>
## 10              <NA>           <NA> 10.8835460764743                 <NA>
## 11              <NA>           <NA>             <NA>                 <NA>
## 12              <NA>           <NA>             <NA>                 <NA>
## 13              <NA>           <NA>             <NA>                 <NA>
## 14              <NA>           <NA>             <NA>                 <NA>
## 15              <NA>           <NA>             <NA>                 <NA>
## 16              <NA>           <NA>             <NA>                 <NA>
## 17              <NA>           <NA>             <NA>                 <NA>
## 18              <NA>           <NA>             <NA>                 <NA>
## 19              <NA>           <NA>             <NA>                 <NA>
## 20              <NA>           <NA>             <NA>                 <NA>
## 21              <NA>           <NA>             <NA>                 <NA>
## 22              <NA>           <NA>             <NA>                 <NA>
## 23              <NA>           <NA>             <NA>                 <NA>
## 24              <NA>           <NA>             <NA>                 <NA>
## 25              <NA>           <NA>             <NA>                 <NA>
## 26              <NA>           <NA>             <NA>                 <NA>
## 27              <NA>           <NA>             <NA>                 <NA>
## 28              <NA>           <NA>             <NA>                 <NA>
## 29              <NA>           <NA>             <NA>                 <NA>
## 30              <NA>           <NA>             <NA>                 <NA>
## 31              <NA>           <NA>             <NA>                 <NA>
## 32              <NA>           <NA>             <NA> 0.000389164756361538
## 33              <NA>           <NA>             <NA>                 <NA>
## 34              <NA>           <NA>             <NA> 0.000534067604123492
## 35              <NA>           <NA>             <NA>                 <NA>
## 36              <NA>           <NA>             <NA>                 <NA>
## 37              <NA>           <NA>             <NA> 0.000638714897400409
## 38              <NA>           <NA>             <NA>                 <NA>
## 39              <NA>           <NA>             <NA>                 <NA>
## 40              <NA>           <NA>             <NA>                 <NA>
## 41              <NA>           <NA>             <NA>                 <NA>
## 42              <NA>           <NA>             <NA>                 <NA>
## 43              <NA>           <NA>             <NA>                 <NA>
## 44              <NA>           <NA>             <NA>                 <NA>
## 45              <NA>           <NA>             <NA>                 <NA>
## 46              <NA>           <NA>             <NA>                 <NA>
## 47              <NA>           <NA>             <NA>                 <NA>
## 48              <NA>           <NA>             <NA>                 <NA>
## 49              <NA>           <NA>             <NA>                 <NA>
## 50              <NA>           <NA>             <NA>                 <NA>
## 51              <NA>           <NA>             <NA>                 <NA>
## 52              <NA>           <NA>             <NA>                 <NA>
## 53              <NA>           <NA>             <NA>                 <NA>
## 54              <NA>           <NA>             <NA>                 <NA>
## 55              <NA>           <NA>             <NA>                 <NA>
## 56              <NA>           <NA>             <NA>                 <NA>
## 57              <NA>           <NA>             <NA>                 <NA>
## 58              <NA>           <NA>             <NA>                 <NA>
## 59              <NA>           <NA>             <NA>                 <NA>
## 60              <NA>           <NA>             <NA>                 <NA>
## 61              <NA>           <NA>             <NA>                 <NA>
## 62              <NA>           <NA>             <NA>                 <NA>
## 63              <NA>           <NA>             <NA>                 <NA>
## 64              <NA>           <NA>             <NA>                 <NA>
## 65              <NA>           <NA>             <NA>                 <NA>
## 66              <NA>           <NA>             <NA>                 <NA>
## 67              <NA>           <NA>             <NA>                 <NA>
## 68              <NA>           <NA>             <NA>                 <NA>
## 69              <NA>           <NA>             <NA>                 <NA>
## 70              <NA>           <NA>             <NA>                 <NA>
## 71              <NA>           <NA>             <NA>                 <NA>
## 72              <NA>           <NA>             <NA>                 <NA>
## 73              <NA>           <NA>             <NA>                 <NA>
## 74              <NA>           <NA>             <NA>                 <NA>
## 75              <NA>           <NA>             <NA>                 <NA>
## 76              <NA>           <NA>             <NA>                 <NA>
## 77              <NA>           <NA>             <NA>                 <NA>
## 78              <NA>           <NA>             <NA>                 <NA>
## 79              <NA>           <NA>             <NA>                 <NA>
## 80              <NA>           <NA>             <NA>                 <NA>
## 81              <NA>           <NA>             <NA>                 <NA>
## 82              <NA>           <NA>             <NA>                 <NA>
## 83              <NA>           <NA>             <NA>                 <NA>
## 84              <NA>           <NA>             <NA>                 <NA>
## 85              <NA>           <NA>             <NA>                 <NA>
## 86              <NA>           <NA>             <NA>                 <NA>
## 87              <NA>           <NA>             <NA>                 <NA>
## 88              <NA>           <NA>             <NA>                 <NA>
## 89              <NA>           <NA>             <NA>                 <NA>
## 90              <NA>           <NA>             <NA>                 <NA>
## 91              <NA>           <NA>             <NA>                 <NA>
## 92              <NA>           <NA>             <NA>                 <NA>
## 93              <NA>           <NA>             <NA>                 <NA>
## 94              <NA>           <NA>             <NA>                 <NA>
## 95              <NA>           <NA>             <NA>                 <NA>
## 96              <NA>           <NA>             <NA>                 <NA>
## 97              <NA>           <NA>             <NA>                 <NA>
## 98              <NA>           <NA>             <NA>                 <NA>
## 99              <NA>           <NA>             <NA>                 <NA>
## 100             <NA>           <NA>             <NA>                 <NA>
## 101             <NA>           <NA>             <NA>                 <NA>
## 102             <NA>           <NA>             <NA>                 <NA>
## 103             <NA>           <NA>             <NA>                 <NA>
## 104             <NA>           <NA>             <NA>                 <NA>
## 105             <NA>           <NA>             <NA>                 <NA>
## 106             <NA>           <NA>             <NA>                 <NA>
## 107             <NA>           <NA>             <NA>                 <NA>
## 108             <NA>           <NA>             <NA>                 <NA>
## 109             <NA>           <NA>             <NA>                 <NA>
## 110             <NA>           <NA>             <NA>                 <NA>
## 111             <NA>           <NA>             <NA>                 <NA>
## 112             <NA>           <NA>             <NA>                 <NA>
## 113             <NA>           <NA>             <NA>                 <NA>
## 114             <NA>           <NA>             <NA>                 <NA>
## 115             <NA>           <NA>             <NA>                 <NA>
## 116             <NA>           <NA>             <NA>                 <NA>
## 117             <NA>           <NA>             <NA>                 <NA>
## 118             <NA>           <NA>             <NA>                 <NA>
##     expensive_ns_stderr
## 1                  <NA>
## 2                  <NA>
## 3                  <NA>
## 4       2.7525488850331
## 5      3.18362681970739
## 6        2.685004429774
## 7      3.14718649855302
## 8      3.36659651180983
## 9       5.6442382368554
## 10                 <NA>
## 11                 <NA>
## 12                 <NA>
## 13                 <NA>
## 14                 <NA>
## 15                 <NA>
## 16                 <NA>
## 17                 <NA>
## 18                 <NA>
## 19                 <NA>
## 20                 <NA>
## 21                 <NA>
## 22                 <NA>
## 23                 <NA>
## 24                 <NA>
## 25                 <NA>
## 26                 <NA>
## 27                 <NA>
## 28                 <NA>
## 29                 <NA>
## 30                 <NA>
## 31                 <NA>
## 32                 <NA>
## 33                 <NA>
## 34                 <NA>
## 35                 <NA>
## 36                 <NA>
## 37                 <NA>
## 38                 <NA>
## 39                 <NA>
## 40                 <NA>
## 41                 <NA>
## 42                 <NA>
## 43                 <NA>
## 44                 <NA>
## 45                 <NA>
## 46                 <NA>
## 47                 <NA>
## 48                 <NA>
## 49                 <NA>
## 50                 <NA>
## 51                 <NA>
## 52                 <NA>
## 53                 <NA>
## 54                 <NA>
## 55                 <NA>
## 56                 <NA>
## 57                 <NA>
## 58                 <NA>
## 59                 <NA>
## 60                 <NA>
## 61                 <NA>
## 62                 <NA>
## 63                 <NA>
## 64                 <NA>
## 65                 <NA>
## 66                 <NA>
## 67                 <NA>
## 68                 <NA>
## 69                 <NA>
## 70                 <NA>
## 71                 <NA>
## 72                 <NA>
## 73                 <NA>
## 74                 <NA>
## 75                 <NA>
## 76                 <NA>
## 77                 <NA>
## 78                 <NA>
## 79                 <NA>
## 80                 <NA>
## 81                 <NA>
## 82                 <NA>
## 83                 <NA>
## 84                 <NA>
## 85                 <NA>
## 86                 <NA>
## 87                 <NA>
## 88                 <NA>
## 89                 <NA>
## 90                 <NA>
## 91                 <NA>
## 92                 <NA>
## 93                 <NA>
## 94                 <NA>
## 95                 <NA>
## 96                 <NA>
## 97                 <NA>
## 98                 <NA>
## 99                 <NA>
## 100                <NA>
## 101                <NA>
## 102                <NA>
## 103                <NA>
## 104                <NA>
## 105                <NA>
## 106                <NA>
## 107                <NA>
## 108                <NA>
## 109                <NA>
## 110                <NA>
## 111                <NA>
## 112                <NA>
## 113                <NA>
## 114                <NA>
## 115                <NA>
## 116                <NA>
## 117                <NA>
## 118                <NA>
write.csv(estimates, paste0("../../local/", env, "_argument_estimated_cost.csv"), quote=FALSE, row.names=FALSE)